A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease
Open accessPublished version of a preprint
Abstract
A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
49 of 62 cited sources checked · 13 supported · 0 flagged · accuracy not yet measured
- Findingown data
In HL60 cells, AD heritability showed significant enrichment for SPI1 (PU.1) that was much stronger than for POLR2AphosphoS5.
“We observed a significant enrichment for SPI1 (PU.1) (34.58 fold enrichment, P=1.31×10−3 in first replicate; 58.12 fold enrichment, P=4.95×10−3 in second replicate) much stronger than that for POLR2AphosphoS5 (15.78 fold enrichment, P=1.71×10−2 in first replicate; 16.34 fold enrichment, P=1.25×10−1 in second replicate)”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [37] Sakai, K. et al. A neuronal VLDLR variant lacking the third complement-type repeat exhibits high capacity binding of apoE containing lipoproteins. Brain Res. 1276, 11–21 (2009). source
The Results (§2.5) concern VLDLR splicing in mouse neurons and astrocytes, not AD heritability enrichment in HL60 cells. Searches for SPI1/PU.1, POLR2AphosphoS5, and the reported fold-enrichment values found no passage addressing the claim; no page numbers were available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measured
- Premisecitation
PU.1 binds to active enhancers of Trem2 and Tyrobp in ChIP-Seq experiments using mouse BV2 cells or bone marrow-derived mouse macrophages.
“Further, PU.1 binds to active enhancers of Trem2 and Tyrobp in ChIP-Seq experiments using mouse BV2 cells42 or bone marrow-derived mouse macrophages43.”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [42] Daniel, B. et al. The active enhancer network operated by liganded RXR supports angiogenic activity in macrophages. Genes Dev. 28, 1562–1577 (2014). source
The Results section on mapping cistromes in BMDMs reports PU.1 ChIP-seq peaks and active histone marks in bone marrow-derived macrophages. Our copy does not identify PU.1 binding at enhancers of Trem2 or Tyrobp, so it does not support the gene-specific part of the claim. The BV2-cell experiment is attributed to a different citation in the citing sentence. (LLM, conf=0.83)
Experimental — accuracy not yet measured - cites [43] McKercher, S.R. et al. Targeted disruption of the PU.1 gene results in multiple hematopoietic abnormalities. EMBO J. 15, 5647–5658 (1996). source
- Findingcitation
SPI1, MS4A4A, MS4A6A, and SELL are specifically expressed in microglia based on RNA-Seq data.
“Further, SPI1, MS4A4A, MS4A6A, and SELL are specifically expressed in microglia based on RNA-Seq data29–31”
Quote in the paperSubstantial claim3 of 3 sources checked- cites [29] Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014). source
The section ‘RNA-Seq analysis revealed cell type-specific gene expression profiles’ reports microglia-enriched genes, but the passages in our copy do not identify SPI1, MS4A4A, MS4A6A, or SELL as among them. Searches for the four gene names and their mouse-style capitalization found no passage establishing their microglia-specific expression; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [30] Bennett, M.L. et al. New tools for studying microglia in the mouse and human CNS. Proc. Natl. Acad. Sci. USA 113, E1738–E1746 (2016). source
The sections on Tmem119-purified transcriptomes and microglial maturation describe RNA-seq profiling and identify other microglia-enriched genes, but do not identify SPI1, MS4A4A, MS4A6A, or SELL as specifically expressed in microglia. Searches for the four genes, including mouse-style capitalization, found no passage establishing the claim. Page numbers were not available in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measured - cites [31] Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014). source
The Materials and Methods section (Expression dataset; page unavailable) describes gene-expression arrays from human liver samples, not microglial RNA-Seq. Searches for SPI1, MS4A4A, MS4A6A, SELL, and microglial RNA-Seq found no passage in our copy addressing the claimed cell-specific expression. (LLM, conf=0.76)
Experimental — accuracy not yet measured
- Premisecitation
Genes expressed in myeloid cells display abnormal patterns of chromatin modification in AD mouse models and human samples.
“At the epigenetic level, genes expressed in myeloid cells display abnormal patterns of chromatin modification in AD mouse models and human samples18–20.”
Quote in the paperSubstantial claim3 of 3 sources checked- cites [18] Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013). source
The Results section reports altered gene expression in human Alzheimer’s brain tissue and in mouse microglia after TYROBP perturbation, including down-regulation of genes involved in histone assembly. It does not report abnormal chromatin modifications in myeloid-cell genes in AD mouse models or human samples. Searches for chromatin modification, histone methylation, and acetylation found no passage establishing that claim; page numbers were unavailable in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured - cites [19] Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature 518, 365–369 (2015). source
The paper’s epigenome analysis and Extended Data Figures 4–5 report altered histone marks at regulatory regions in the AD mouse model, alongside increased immune-gene expression. Its human analyses compare gene expression in AD patients and identify immune-cell enhancer activity at regions corresponding to the altered mouse enhancers; they do not show abnormal chromatin modifications measured in human AD samples. No page numbers are provided in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measured - cites [20] Chan, G. et al. CD33 modulates TREM2: convergence of Alzheimer loci. Nat. Neurosci. 18, 1556–1558 (2015). source
The Abstract reports protein-expression and genetic-association findings in human monocytes, not abnormal chromatin modification in myeloid-cell genes. Searches for chromatin modification and histone changes in AD mouse models and human samples found no passage addressing the claim in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
- Premisecitation
AD-risk alleles are polarized for cis-eQTL effects in monocytes.
“Further, AD-risk alleles are polarized for cis-expression quantitative trait locus (cis-eQTL) effects in monocytes21.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [21] Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014). source
- Interpretationown data
Colocalization analyses using coloc and SMR/HEIDI support the hypothesis that the same causal SNP(s) influence SPI1 expression and AD risk.
“Colocalization analyses using coloc32 and SMR/HEIDI23 support the hypothesis that the same causal SNP(s) influence SPI1 expression and AD risk.”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [32] Hikami, K. et al. Association of a functional polymorphism in the 3′-untranslated region of SPI1 with systemic lupus erythematosus. Arthritis Rheum. 63, 755–763 (2011). source
- cites [23] GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013). source
The Abstract discusses eQTLs generally, and the Statistical analysis and methods development section describes planned GTEx analyses. Neither reports coloc or SMR/HEIDI colocalization results, SPI1 expression, or Alzheimer’s disease risk. Searches for those terms and for shared causal SNPs found no passage addressing the claim. (LLM, conf=0.75)
Experimental — accuracy not yet measured
- Interpretationown data
The findings strengthen the growing evidence linking AD to a network of genes expressed in microglia.
“These results reinforce the emerging genetic and epigenetic association between AD and a network of microglial expressed genes2,5,17–21”
Quote in the paperNot a substantial claim7 of 7 sources checked- cites [2] 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). source
The paper’s Abstract (page unavailable in our copy) reports genome-wide significant associations between late-onset AD and several genes, including MS4A4A and CD33. It does not identify these genes as a microglial-expressed network or report an epigenetic association, so it supports the genetic-association part but not those added characterizations. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites [5] 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). source
The paper’s Abstract reports genetic associations with several AD susceptibility loci and says ABCA7 is highly expressed in microglia. It also notes putative immune functions for several associated genes, but does not establish that they form a network of microglia-expressed genes or report an epigenetic association. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [17] Bradshaw, E.M. et al. CD33 Alzheimer's disease locus: altered monocyte function and amyloid biology. Nat. Neurosci. 16, 848–850 (2013). source
- cites [18] Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013). source
The Results section, “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page unavailable), links an immune/microglia gene network to late-onset AD and describes convergent genetic evidence. The citing sentence also attributes an epigenetic association to the citation group, but the passages in our copy do not establish that component. (LLM, conf=0.83)
Experimental — accuracy not yet measured - cites [19] Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature 518, 365–369 (2015). source
- cites [20] Chan, G. et al. CD33 modulates TREM2: convergence of Alzheimer loci. Nat. Neurosci. 18, 1556–1558 (2015). source
The paper’s introductory passage (page unavailable) links AD susceptibility variants to innate immune genes and identifies an AD-associated microglial transcriptomic network. Its own results examine interactions among AD loci chiefly in monocytes, supporting the broader link in the claim. The cited paper does not report an epigenetic association, which is also asserted in the verbatim citing sentence. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites [21] Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014). source
The paper reports that AD susceptibility alleles are enriched for monocyte-specific effects and suggests that monocytes may be proxies for resident microglia (Abstract; page unavailable). This supports a genetic link to myeloid cells, but it does not establish an AD-associated network of microglial-expressed genes or an epigenetic association specific to that network. (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Premisecitation
PU.1 expression levels regulate multiple functions of myeloid and microglial cells, including proliferation, survival, and differentiation.
“PU.1 expression levels regulate multiple myeloid/microglial cell functions47, including proliferation, survival and differentiation”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [47] Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014). source
The Results section (“Identification of a unique microglial signature”; page unavailable) identifies PU.1 as differentially expressed in microglia and says PU.1-deficient mice do not develop myeloid cells, supporting a role in development. It does not show that PU.1 expression levels regulate proliferation or cell survival, or establish the claimed range of functions. (LLM, conf=0.86)
Experimental — accuracy not yet measured
- Premisecitation
The Cox model has previously been shown to be applicable to case-control datasets without an elevated type 1 error rate or overestimation of effect sizes.
“The Cox model has previously been shown to be applicable to case-control datasets without an elevated type 1 error rate nor overestimation in effect sizes73,74.”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [73] van der Net, J.B. et al. Cox proportional hazards models have more statistical power than logistic regression models in cross-sectional genetic association studies. Eur. J. Hum. Genet. 16, 1111–1116 (2008). source
- cites [74] Pruim, R.J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010). source
The Introduction and Implementation sections describe LocusZoom as a tool for visualizing genome-wide association results, not for evaluating Cox models. Searches for Cox models, case-control datasets, type I error, and effect-size overestimation found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
- Findingown data
rs4803758 near APOE is associated with CSF phosphorylated tau181 levels.
“Rs4803758 near APOE showed the most significant associations with levels of CSF phosphorylated tau181 (P=3.75×10−4) and CSF Aβ42 (P=3.12×10−5)”
Quote in the paperSubstantial claim - Findingown data
rs4803758 near APOE is associated with CSF Aβ42 levels.
“Rs4803758 near APOE showed the most significant associations with levels of CSF phosphorylated tau181 (P=3.75×10−4) and CSF Aβ42 (P=3.12×10−5)”
Quote in the paperSubstantial claim - Findingown data
Four other previously reported AD risk loci (CR1, SPI1/CELF1, SORL1, FERMT2) showed suggestive significance for AAOS.
“Four other AD risk loci previously reported in the IGAP GWAS1 showed associations that reached suggestive significance (P < 1.0×10−5): CR1 (P=1.2×10−6), SPI1/CELF1 (P=5.4×10−6), SORL1 (P=1.8×10−7), and FERMT2 (P=1.0×10−5).”
Quote in the paperNot a substantial claim - Findingown data
rs2093761 is associated with CR1/CR1L expression in white matter.
“rs2093761 was associated with CR1/CR1L expression in white matter (P=1.30×10−7”
Quote in the paperSubstantial claim - Findingown data
rs7445192 is associated with SRA1 expression averaged across brain regions.
“rs7445192 was associated with SRA1 expression averaged across brain regions (P=7.0×10−9, 1.6×10−7 for two probes respectively)”
Quote in the paperNot a substantial claim - Findingown data
Four loci reached genome-wide significance for AAOS: BIN1, MS4A, PICALM, and APOE.
“Four loci showed genome-wide significant associations (P < 5×10−8) with AAOS: BIN1 (P=7.6×10−13), MS4A (P=5.1×10−11), PICALM (P=4.3×10−14), and APOE (P=1.2×10−67)”
Quote in the paperNot a substantial claim - Findingown data
AD heritability is significantly enriched in hematopoietic cells of the myeloid and B-lymphoid lineage.
“We found a significant enrichment of AD heritability in hematopoietic cells of the myeloid and B-lymphoid lineage (e.g., 14.49 fold enrichment, P=3.49×10−5 in monocytes/CD14 enhancers/H3K4me1 and 12.33 fold enrichment, P=1.41×10−6 in B-cells/CD19 enhancers/H3K4me1).”
Quote in the paperSubstantial claim - Findingown data
rs5750677 is associated with lower SUN2 expression in monocytes and macrophages.
“rs5750677 was significantly associated with lower expression of SUN2 in both monocytes (P=3.66×10−58) and macrophages (P=3.15×10−36)”
Quote in the paperNot a substantial claim - Findingown data
rs1057233 is associated with MTCH2 expression in cerebellum.
“rs1057233 was associated with MTCH2 expression in the cerebellum (P=1.20×10−9)”
Quote in the paperNot a substantial claim - Findingown data
The minor allele C of rs7930318 is associated with lower MS4A4A expression in monocytes.
“the minor allele (C) of rs7930318 was consistently associated with lower expression of MS4A4A in monocytes (P=8.20×10−28)”
Quote in the paperNot a substantial claim - Findingown data
The rs1057233G allele is associated with lower NUP160 expression in macrophages.
“lower NUP160 expression in macrophages (P=5.35×10−22)”
Quote in the paperNot a substantial claim - Findingown data
Three of the 14 novel AAOS loci (rs116341973, rs1625716, rs11074412) were nominally associated with AD risk in IGAP GWAS.
“3 of which (rs116341973, rs1625716, and rs11074412) were nominally associated with AD risk (Bonferroni-corrected threshold: P=0.05/22=2.27×10−3) in the IGAP GWAS”
Quote in the paperNot a substantial claim - Findingown data
The protective rs1057233G allele is strongly associated with lower SPI1 expression in monocytes and macrophages.
“the protective rs1057233G allele, located within the 3′ UTR of SPI1, was strongly associated with lower expression of SPI1 in both monocytes (P=1.50×10−105) and macrophages (P=6.41×10−87)”
Quote in the paperSubstantial claim - Findingown data
The rs1057233G allele is associated with higher CELF1 expression in monocytes.
“higher expression of CELF1 in monocytes (P=3.95×10−8)”
Quote in the paperNot a substantial claim - Findingown data
rs1057233 in the SPI1/CELF1 locus is significantly associated with CSF Aβ42.
“rs1057233 in the SPI1/CELF1 locus was significantly associated with CSF Aβ42 (P=8.24×10−4).”
Quote in the paperSubstantial claim - Findingown data
The same survival analysis identified fourteen novel loci associated with age at onset.
“A genome-wide survival analysis of 14,406 Alzheimer’s disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and fourteen novel loci associated with age at onset.”
- Also stated in: § Genome-wide survival analysis · plain
“We also identified 14 novel loci that reached suggestive significance in the survival analysis”
Quote in the paperNot a substantial claim - Also stated in: § Genome-wide survival analysis · plain
- Findingown data
The minor allele C of rs7930318 is associated with lower MS4A6A expression in monocytes and macrophages.
“and MS4A6A in monocytes (P=4.90×10−23) and macrophages (P=1.25×10−9”
Quote in the paperNot a substantial claim - Findingown data
The minor allele of rs7930318 near MS4A4A is associated with delayed AAO.
“The minor allele of rs7930318 near MS4A4A is associated with delayed AAO.”
Quote in the paperSubstantial claim - Findingown data
The MS4A locus is associated with an AAO-related phenotype.
“this is the first time that the MS4A locus is reported to be associated with an AAO-related phenotype.”
Quote in the paperSubstantial claim - Findingown data
Conditioning for rs1057233 or rs10838698 abolished the associations of rs1377416 and rs10838725 with SPI1 expression.
“However, conditioning for either of these two SNPs abolished the associations of rs1377416 and rs10838725 with SPI1 expression (Supplementary Table 9).”
Quote in the paperSubstantial claim - Findingown data
rs1057233 is strongly associated with SPI1 expression in the replication dataset.
“We found strong evidence for the association between rs1057233 and SPI1 expression (P=6.39×10−102) as well as MYBPC3 expression (P=5.95×10−33”
Quote in the paperSubstantial claim - Findingown data
rs1057233 and rs10838698 remained significantly associated with SPI1 expression when adjusting for either of the other two SNPs in both monocytes and macrophages.
“Rs1057233 and rs10838698 remained significantly associated with SPI1 expression when adjusting for either of the other two SNPs in both monocytes and macrophages (P < 8.33×10−3).”
Quote in the paperSubstantial claim - Findingown data
In both monocytes and macrophages, SPI1 was consistently identified as the most likely gene whose expression levels are associated with AD survival because of causality/pleiotropy at the same underlying causal variant.
“In both monocytes and macrophages, SPI1 was consistently identified as the most likely gene whose expression levels are associated with AD survival because of causality/pleiotropy at the same underlying causal variant (rs1057233, rs10838698, rs10838699, or rs7928163 in the same LD block) (SMR P < 4.90E-04, Bonferroni-corrected threshold for 6 SNPs tested against 17 probes and HEIDI P ≥ 0.05”
Quote in the paperSubstantial claim - Findingown data
Multiple SNPs in tight LD with rs1057233 (e.g., rs10838699 and rs7928163) changed the predicted DNA binding motif of SPI1 (PU.1).
“we identified multiple SNPs (e.g, rs10838699 and rs7928163) in tight LD with rs1057233 that changed the predicted DNA binding motif of SPI1 (PU.1)”
Quote in the paperSubstantial claim - Findingown data
Phagocytic activity was not changed in untransfected cells when analyzed by flow cytometry.
“Phagocytic activity was not changed in untransfected cells when analyzed by flow cytometry (Supplementary Fig. 8d, 8e, 8f, 8g).”
Quote in the paperNot a substantial claim - Findingown data
The association landscape in the AD survival analysis highly resembles that of SPI1 cis-eQTL analysis in myeloid cells.
“The association landscape in the AD survival analysis highly resembles that of SPI1 cis-eQTL analysis in myeloid cells (Fig. 1a).”
Quote in the paperSubstantial claim - Findingown data
Cis-eQTL associations with SPI1, MYBPC3, MS4A4A, MS4A6A, and SELL were replicated in an independent dataset of primary CD14+ human monocytes.
“We replicated cis-eQTL associations with expression of SPI1, MYBPC3, MS4A4A, MS4A6A, and SELL (Bonferroni-corrected threshold: P=0.05/15421 probes = 3.24×10−6).”
Quote in the paperSubstantial claim - Findingown data
Neither conditional analysis nor SMR/HEIDI analysis could definitively identify a single functional variant among the set of 4 SNPs in high LD.
“Neither conditional analysis nor SMR/HEIDI analysis could definitively identify a single functional variant among this set of 4 SNPs in high LD.”
Quote in the paperNot a substantial claim - Findingown data
rs1057233 is strongly associated with lower SPI1 expression in human monocytes and macrophages.
“Furthermore, it is strongly associated with lower SPI1 expression in human monocytes (P=1.50×10−105) and macrophages (P=6.41×10−87, Table 3).”
Quote in the paperSubstantial claim - Findingown data
rs10919252 is associated with lower SELL expression in monocytes.
“rs10919252 was associated with lower expression of SELL in monocytes (P=7.33×10−35)”
Quote in the paperNot a substantial claim - Findingown data
rs1530914 and rs7929589, in high LD with rs7930318, are associated with MS4A4A and MS4A6A expression.
“Rs1530914 and rs7929589, both in high LD with rs7930318 (R2 = 0.99 and 0.87, respectively), were associated with expression of MS4A4A and MS4A6A (P=3.60×10−8, 6.37×10−15), respectively.”
Quote in the paperSubstantial claim - Findingown data
rs2272918, tagging rs10919252, is associated with SELL expression.
“rs2272918, tagging rs10919252, was significantly associated with expression of SELL (P=8.43×10−16).”
Quote in the paperNot a substantial claim - Findingown data
rs1625716 is associated with lower CISD1 expression in macrophages.
“rs1625716 was associated with lower expression of CISD1 in macrophages (P=5.98×10−23”
Quote in the paperNot a substantial claim - Findingown data
The transformed and standardized CSF values did not differ significantly across cohorts.
“No significant differences in the transformed and standardized CSF values were found between cohorts.”
Quote in the paperSubstantial claim - Findingown data
The candidate SNPs showed consistent effect sizes across the 3 retrospective case-control cohorts (ADGC, GERAD, EADI case-control) and the 4 prospective cohorts (EADI-prospective, CHARGE FHS, CHS and Rotterdam).
“We also examined the effect sizes of our candidate SNPs in these cohorts and found consistent effect sizes (Supplementary Fig. 3) in the 3 retrospective case-control cohorts (ADGC, GERAD, EADI case-control) and 4 prospective cohorts (EADI-prospective, CHARGE FHS, CHS and Rotterdam).”
Quote in the paperNot a substantial claim - Findingown data
In BV2 cells, increasing and decreasing Spi1 caused different, often opposite, changes in the expression of the profiled genes.
“overexpression and knock-down of Spi1 in BV2 cells produce different and often opposite changes in expression of the genes profiled here”
Quote in the paperNot a substantial claim - Findingown data
Knockdown of Spi1 elevated expression of Cx3cr1 and Axl.
“expression of Cx3cr1 and Axl were elevated upon knock-down of Spi1 (Fig. 4c)”
Quote in the paperNot a substantial claim - Findingreasoning
Reducing SPI1 expression also decreases MS4A4A and MS4A6A expression.
“lowering SPI1 expression, which is protective for AD risk, also lowers MS4A4A and MS4A6A expression.”
Quote in the paperNot a substantial claim - Findingown data
The allele that lowers AD risk is linked to decreased expression of MS4A4A and MS4A6A.
“the allele associated with reduced AD risk is associated with lower MS4A4A and MS4A6A expression.”
Quote in the paperSubstantial claim - Findingown data
The level of Spi1 influences the expression of Ccl2 and Cxcl2.
“Spi1 levels modulate expression of Ccl2 and Cxcl2 (Fig. 4a)”
Quote in the paperSubstantial claim - Findingown data
A combined analysis of AD survival and endophenotype GWAS replicated and identified several genetic loci linked to AAO.
“by combining AD survival and endophenotype GWAS analyses, we replicated and discovered multiple genetic loci associated with AAOAAOS.”
Quote in the paperSubstantial claim - Findingown data
Reducing Spi1 decreased the expression of Aif1 (Iba1), Il1b, Nos2, Ptgs2, Arg1, and Nlrp3.
“knocking down Spi1 reduced expression of a microgliosis marker Aif1 (Iba1) along with Il1b, Nos2, Ptgs2, Arg1 and Nlrp3 (Fig. 4a, 4c)”
Quote in the paperSubstantial claim - Findingown data
SNPs linked to AAOS in the MS4A cluster show eQTL associations in myeloid cells for MS4A4A and MS4A6A.
“we demonstrate that AAOS-associated SNPs within the MS4A gene cluster are associated with eQTLs in myeloid cells for both MS4A4A and MS4A6A.”
Quote in the paperSubstantial claim - Interpretationown data
Myeloid cells specifically modulate AD susceptibility.
“These results suggest that myeloid cells specifically modulate AD susceptibility.”
Quote in the paperSubstantial claim - Interpretationown data
PU.1 may regulate the expression of multiple AD-associated genes in myeloid cells.
“Together, these results suggest that PU.1 may regulate the expression of multiple AD-associated genes in myeloid cells.”
Quote in the paperSubstantial claim - Interpretationown data
Lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.
“Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Cis-eQTL effects observed in monocytes and macrophages reflect regulation in brain microglia relevant to AD.
- Rests on an assumption: Mouse orthologs of human AD risk genes have conserved regulation by Spi1.
- Interpretationreasoning
One or more SNPs in very high LD with rs1057233 could explain the observed associations with SPI1 expression and AD-related phenotypes.
“Based on these results, one or more of these or other SNPs in very high LD with rs1057233, could explain the observed associations with SPI1 expression and AD-related phenotypes.”
Quote in the paperSubstantial claim - Interpretationown data
Modulation of PU.1 expression levels significantly changes microglial phagocytic activity in response to fungal targets (mimicked by zymosan).
“These data suggest that modulation of PU.1 expression levels significant changes microglial phagocytic activity in response to fungal targets (mimicked by zymosan).”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Zymosan phagocytosis is a valid proxy for microglial phagocytic activity relevant to AD pathogenesis.
- Interpretationown data
Several genes with roles in phagocytosis are dysregulated by altering Spi1 expression, suggesting a collective and coordinated effect of Spi1 on phagocytic activity of BV2 cells.
“In addition to Cd33, Tyrobp, Apoe and Clu/ApoJ, several genes with roles in phagocytosis are dysregulated by altering Spi1 expression, i.e. Cd36, Fcgr1, P2ry12, Itgam, Cx3cr1, Axl, Ctsb (Fig. 4a, 4b, 4c), suggesting a collective and coordinated effect of Spi1 on the phagocytic activity of BV2 cells.”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Mouse orthologs of human AD risk genes have conserved regulation by Spi1.
- Interpretationown data
Multiple microglial genes, some already implicated in AD, are selectively perturbed by altered expression of Spi1.
“These data demonstrate that multiple microglial genes, some already implicated in AD, are selectively perturbed by altered expression of Spi1.”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Mouse orthologs of human AD risk genes have conserved regulation by Spi1.
- Interpretationown data
The contribution of the myeloid PU.1 target gene network to disease susceptibility is specific to AD.
“Thus, the contribution of the myeloid PU.1 target gene network to disease susceptibility is specific to AD.”
Quote in the paperSubstantial claim - Interpretationown data
The strong cis-eQTL effects and colocalization results point to SPI1 as the most likely candidate gene underlying the disease association at the SPI1/CELF1 locus.
“The strong cis-eQTL effects and colocalization results point to SPI1 as the most likely candidate gene underlying the disease association at the SPI1/CELF1 locus.”
Quote in the paperSubstantial claim - Interpretationown data
Given its selective expression in microglia in the brain, PU.1 may modify microglial cell function through transcriptional regulation of target genes that act as downstream modulators of AD susceptibility.
“Given its selective expression in microglia in the brain (Fig. 2b), PU.1 may modify microglial cell function through transcriptional regulation of target genes that act as downstream modulators of AD susceptibility, as evidenced by the significant enrichment of AD heritability partitioned on the PU.1 cistrome in human myeloid cells”
Quote in the paperSubstantial claim - Interpretationown data
The functional variant(s) mediating the effect on SPI1 expression and AD risk likely reside in the LD block that includes rs1057233 and rs10838698 but not rs10838725 and rs1377416.
“Thus, the functional variant(s) mediating the effect on SPI1 expression and AD risk likely reside(s) in the LD block that includes rs1057233 and rs10838698 but not rs10838725 and rs1377416”
Quote in the paperSubstantial claim - Interpretationreasoning
SPI1 may be a master regulator capable of tipping the balance toward a neuroprotective or neurotoxic microglial phenotype.
“Together with genetic variation in myeloid genes associated with AD as an amplifier, SPI1 may be a master regulator capable of tipping the balance toward a neuroprotective or neurotoxic microglial phenotype.”
Quote in the paperSubstantial claim - Interpretationown data
rs1057233 is in stronger LD with the AD risk causal variant than rs10838725.
“This suggests that rs1057233 is in stronger LD with the AD risk causal variant.”
Quote in the paperSubstantial claim - Study design and conductown data
PU.1 overexpression and knock-down were confirmed by western blotting and qPCR.
“We confirmed overexpression and knock-down of PU.1 expression levels by western blotting and qPCR (Fig. 3).”
Quote in the paperSubstantial claim - Study design and conductown data
Only 3 of the 148 Schoenfeld P values were below 0.05, suggesting the time proportionality assumption is unlikely to be violated in these associations.
“Further, only 3 out of the 148 P values were less than 0.05, suggesting that the time proportionality assumption is unlikely to be violated in these associations (Supplementary Table 1).”
Quote in the paperSubstantial claim- The authors state this as an assumption: The Cox model's proportional hazards assumption holds.
- Interpretationown data
SPI1 is nominated as the gene accounting for the disease association at the previously reported CELF1 locus.
“we nominate SPI1 as the gene responsible for disease association at the previously reported CELF1 locus.”
Quote in the paperSubstantial claim - Interpretationown data
Lower SPI1 expression reduces risk for AD.
“Our data suggest that lower SPI1 expression reduces risk for AD”
- Also stated in: § Discussion · plain
“lowering SPI1 expression, which is protective for AD risk”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Classified as probably not a substantial claim. 44
- May combine two claims. 45
- The quote only partly states this claim. 1
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTranscriptomic and proteomic analyses of microglial cells suggested a microglial homeostatic signature that is perturbed during aging and under pathological conditions.
“Transcriptomic and proteomic analyses of microglial cells suggested a microglial homeostatic signature that is perturbed during aging and under pathological conditions48.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [48] Wang, Y. et al. IL-34 is a tissue-restricted ligand of CSF1R required for the development of Langerhans cells and microglia. Nat. Immunol. 13, 753–760 (2012). source
The Results section on IL-34 deficiency and the Discussion address microglial abundance, CNS protection and possible roles in neurodegenerative pathology (page unavailable). Searches for transcriptomic or proteomic analyses, a homeostatic signature and aging found no passage addressing the claimed signature or its perturbation. (LLM, conf=0.77)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrevious data showed decreased uptake of Aβ42 in primary microglial cells isolated from adult human brain tissue and transfected with siRNA targeting SPI1.
“This is in line with previous data showing decreased uptake of Aβ42 (also a TLR2 agonist) in primary microglial cells isolated from adult human brain tissue and transfected with siRNA targeting SPI147.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [47] Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014). source
The Methods section on human microglia isolation and cell culture describes isolating cells from adult human brain tissue. Searches for SPI1, siRNA, Aβ42 uptake, and related terms found no passage reporting the claimed transfection or decreased uptake; the available copy therefore does not verify the result. (LLM, conf=0.73)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationVLDLR is a receptor for lipoproteins containing APOE and CLU/APOJ.
“VLDLR is a receptor for lipoproteins containing APOE38 and CLU/APOJ39, another AD risk gene.”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [38] Bajari, T.M., Strasser, V., Nimpf, J. & Schneider, W.J. A model for modulation of leptin activity by association with clusterin. FASEB J. 17, 1505–1507 (2003). source
- cites [39] Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010). source
The Results sections on functional variants and pathways discuss BMI-associated genes and other receptors, but do not describe VLDLR as a receptor for APOE- or CLU/APOJ-containing lipoproteins. Searches for VLDLR, CLU/APOJ, and related receptor terms found no passage addressing that relationship; page numbers were unavailable in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPU.1 is required for the development and function of myeloid and B-lymphoid cells.
“PU.1 is required for the development and function of myeloid and B-lymphoid cells44,45.”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [44] Beers, D.R. et al. Wild-type microglia extend survival in PU.1 knockout mice with familial amyotrophic lateral sclerosis. Proc. Natl. Acad. Sci. USA 103, 16021–16026 (2006). source
- cites [45] Mak, K.S., Funnell, A.P.W., Pearson, R.C.M. & Crossley, M. PU.1 and haematopoietic cell fate: dosage matters. Int. J. Cell Biol. 2011, 808524 (2011). source
Section 4 says PU.1 is required for myeloid differentiation and enables committed cells to respond to myeloid growth factors. Section 5 supports a requirement for B-cell production, but says PU.1 is dispensable after lymphoid commitment: deletion after the pre-B-cell stage does not disrupt immunoglobulin expression or responses to mitogens. The claim therefore overstates its requirement for B-cell function. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
PremisecitationGWAS and sequencing studies have found associations between AD and genes expressed in myeloid cells, including TREM2, ABCA7, and CD33.
“At the genetic level, GWAS and sequencing studies have found associations between AD and genes expressed in myeloid cells, including TREM2, ABCA7, and CD331,2,5,15–17.”
Quote in the paperSubstantial claim6 of 6 sources checked- cites [1] 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). source
The Abstract describes ABCA7 and CD33 as previously identified GWAS genes associated with Alzheimer’s disease and reports a rare TREM2 risk variant. It does not say that all three genes are expressed in myeloid cells. It also notes that this study did not replicate the CD33 association in stage 2 (P = 0.61). Page numbers are not available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measured - cites [2] 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). source
The section labelled Abstract (page unavailable) reports a genome-wide association between CD33 and late-onset AD. It also reports suggestive evidence for ABCA7 in this study and a stronger association reported by Hollingworth et al. Our copy does not establish the TREM2 association, sequencing findings, or expression of these genes in myeloid cells. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [5] 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). source
The study reports GWAS associations with AD for ABCA7 and CD33, and states that ABCA7 is highly expressed in microglia (Abstract; page not supplied). It does not report a TREM2 association or a sequencing finding, and its discussion of CD33’s immune functions does not establish its expression in myeloid cells. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [15] Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer's disease. N. Engl. J. Med. 368, 107–116 (2013). source
The Results report an association between a TREM2 variant and Alzheimer’s disease, and the Discussion identifies TREM2 in microglia and CD33 as a myeloid cell-surface receptor linked to Alzheimer’s disease by genomewide association studies. ABCA7 appears in a cited study’s title in the References, but the paper does not discuss its myeloid-cell expression; that part of the claim is not supported by this copy. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [16] Guerreiro, R. et al. TREM2 variants in Alzheimer's disease. N. Engl. J. Med. 368, 117–127 (2013). source
- cites [17] Bradshaw, E.M. et al. CD33 Alzheimer's disease locus: altered monocyte function and amyloid biology. Nat. Neurosci. 16, 848–850 (2013). source
The Abstract says genome-wide studies identified CD33 as an AD susceptibility locus and describes CD33 expression in myeloid cells. It does not address associations involving TREM2 or ABCA7, or sequencing findings for those genes, so it supports only the CD33 portion of the sentence. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
Premisecitationrs1377416 is located in a predicted enhancer in the vicinity of SPI1 and altered enhancer activity when assayed in vitro using a reporter construct transfected in BV2 cells.
“rs1377416, is located in a predicted enhancer in the vicinity of SPI1 and altered enhancer activity when assayed in vitro using a reporter construct transfected in BV2 cells19.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [19] Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature 518, 365–369 (2015). source
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationIdentifying causal genes and mechanisms underlying disease-associated loci requires integrative analyses of expression and epigenetic datasets in disease-relevant cell types.
“Identifying causal genes and mechanisms underlying disease-associated loci requires integrative analyses of expression and epigenetic datasets in disease-relevant cell types14.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [14] Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016). source
The Discussion (page unavailable) describes integrating genetic and expression data to identify genes associated with complex traits, and the Results report tissue-specific findings. The paper does not say that identifying causal genes requires epigenetic datasets; its Discussion also cautions that some associations may be indistinguishable from confounding without further analysis. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim. May combine two claims.
PremisecitationSPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function.
“SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function.”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [44] Beers, D.R. et al. Wild-type microglia extend survival in PU.1 knockout mice with familial amyotrophic lateral sclerosis. Proc. Natl. Acad. Sci. USA 103, 16021–16026 (2006). source
- cites [45] Mak, K.S., Funnell, A.P.W., Pearson, R.C.M. & Crossley, M. PU.1 and haematopoietic cell fate: dosage matters. Int. J. Cell Biol. 2011, 808524 (2011). source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitationrs1057233 was previously found to be associated with systemic lupus erythematosus, body mass index, and proinsulin levels.
“Interestingly, rs1057233 was previously found to be associated with systemic lupus erythematosus33, body mass index40 and proinsulin levels41”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites [33] Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012). source
Citation 33 is attached to the lupus association, not the body-mass-index or proinsulin claims. The USAGE section discusses a lupus GWAS but names rs9271100 and rs9271055, not rs1057233; a search for rs1057233 found no passage linking it to lupus in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured - cites [40] Strawbridge, R.J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011). source
Citation 41 is attached to the proinsulin-level association, not the lupus or BMI associations, which have separate citations. The Results tables report proinsulin associations for other SNPs, but a search for rs1057233 found no passage identifying it or linking it to proinsulin levels in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [41] Satoh, J., Asahina, N., Kitano, S. & Kino, Y. A comprehensive profile of ChIP-seq-based PU.1/Spi1 target genes in microglia. Gene Regul. Syst. Bio. 8, 127–139 (2014). source
The Methods section describes a PU.1 ChIP-Seq dataset in mouse microglial cells, and the Results report Spi1 target genes and pathways (page numbers unavailable). Searches for rs1057233 and for systemic lupus erythematosus, body mass index, and proinsulin found no passage in our copy addressing the claimed associations. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
PremisecitationFew studies have investigated the genetic basis of age at onset of AD.
“For example, few studies6,7 have investigated the genetic basis of age at onset of AD (AAO).”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [6] Naj, A.C. et al. Effects of multiple genetic loci on age at onset in late-onset Alzheimer disease: a genome-wide association study. JAMA Neurol. 71, 1394–1404 (2014). source
- cites [7] Kamboh, M.I. et al. Genome-wide association analysis of age-at-onset in Alzheimer's disease. Mol. Psychiatry 17, 1340–1346 (2012). source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAPOE remains the only locus repeatedly associated with AAO, but PICALM and BIN1 have also been reported to affect AAO.
“To date, APOE remains the only locus repeatedly associated with AAO8,9, but PICALM and BIN1 have also been reported to affect AAO6,10,11.”
Quote in the paperNot a substantial claim4 of 5 sources checked- cites [8] Bennett, C. et al. Evidence that the APOE locus influences rate of disease progression in late onset familial Alzheimer's Disease but is not causative. Am. J. Med. Genet. 60, 1–6 (1995). source
- cites [9] Slooter, A.J. et al. Risk estimates of dementia by apolipoprotein E genotypes from a population-based incidence study: the Rotterdam Study. Arch. Neurol. 55, 964–968 (1998). source
The Comment section reports that APOE genotypes explained some variance in age at onset of dementia, supporting the APOE–AAO association. The paper does not establish that APOE was the only locus repeatedly associated with AAO; its introduction says APOE's role in age at onset was still controversial. PICALM and BIN1 are attached to other citations in the citing sentence, not this one. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [6] Naj, A.C. et al. Effects of multiple genetic loci on age at onset in late-onset Alzheimer disease: a genome-wide association study. JAMA Neurol. 71, 1394–1404 (2014). source
- cites [10] Thambisetty, M., An, Y. & Tanaka, T. Alzheimer's disease risk genes and the age-at-onset phenotype. Neurobiol. Aging 34, 2696 e1–2696.e5 (2013). source
The Discussion (page unavailable) discusses APOE ε4 in relation to cerebral blood flow and Alzheimer’s disease risk, not age at onset (AAO). Searches for PICALM, BIN1, and APOE associations with age at onset found no passage in our copy addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [11] Jones, E.L. et al. Evidence that PICALM affects age at onset of Alzheimer's dementia in Down syndrome. Neurobiol. Aging 34, 2441 e1–2441.e5 (2013). source
The Results (page unavailable) report an association between PICALM variants and age at onset of Alzheimer’s dementia in people with Down syndrome. The Introduction lists BIN1 as an Alzheimer’s disease risk gene, but the paper does not report a BIN1 association with age at onset. The APOE clause has separate citations and is not attributed to this source. (LLM, conf=0.88)
Experimental — accuracy not yet measured
May combine two claims.
Premisecitationrs1057233 changes the target sequence and binding of miR-569.
“rs1057233 changes the target sequence and binding of miR-56933”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [33] Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012). source
The Methods section describes predictions of regulatory motif changes, and the Usage section discusses a different SNP, rs9271055, affecting an Ets-family binding site. Searches for rs1057233 and miR-569 found no passage in our copy addressing their target sequence or binding. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
Study design and conductcitationCases received a diagnosis of dementia of the Alzheimer's type based on NINCDS-ADRDA criteria.
“Cases were diagnosed with dementia of the Alzheimer’s type (DAT) according to the NINCDS-ADRDA20.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [20] Chan, G. et al. CD33 modulates TREM2: convergence of Alzheimer loci. Nat. Neurosci. 18, 1556–1558 (2015). source
The Online Methods describe ROS/MAP participants as cognitively normal at enrollment and receiving annual clinical evaluations, but do not state how Alzheimer’s dementia cases were diagnosed. Searches for NINCDS-ADRDA and dementia diagnosis found no passage specifying those criteria; the passages provide no page numbers. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationIl34 and Csf1 are soluble factors that bind Csf1r and are necessary for microglial development and maintenance in vivo.
“expression of Il34 and Csf1, soluble factors that bind to Csf1r and required for microglial development and maintenance in vivo49”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [49] Dagher, N.N. et al. Colony-stimulating factor 1 receptor inhibition prevents microglial plaque association and improves cognition in 3xTg-AD mice. J. Neuroinflammation 12, 139 (2015). source
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationMonocyte eQTL findings in the Cardiogenics dataset were validated by querying statistics from Fairfax et al.
“For additional monocyte eQTL analysis, we queried statistics from Fairfax et al.28 to validate findings in the Cardiogenics dataset.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [28] Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016). source
The Results section, “Validation of genes differentially expressed by human and mouse” (page unavailable), describes PCR and qPCR validation of astrocyte gene-expression findings, not monocyte eQTLs. Searches for Fairfax, Cardiogenics, and monocyte eQTL validation found no passage addressing the attributed claim. (LLM, conf=0.77)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationCSF collection followed a standardized protocol across all studies.
“CSF in all studies was collected in a standardized manner12,64–67.”
Quote in the paperNot a substantial claim4 of 5 sources checked- cites [12] Cruchaga, C. et al. GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron 78, 256–268 (2013). source
- cites [64] Peskind, E., Nordberg, A., Darreh-Shori, T. & Soininen, H. Safety of lumbar puncture procedures in patients with Alzheimer's disease. Curr. Alzheimer Res. 6, 290–292 (2009). source
- cites [65] Grimmer, T. et al. Beta amyloid in Alzheimer's disease: increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid. Biol. Psychiatry 65, 927–934 (2009). source
- cites [66] Blennow, K., Hampel, H., Weiner, M. & Zetterberg, H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat. Rev. Neurol. 6, 131–144 (2010). source
- cites [67] Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010). source
The Methods and Results sections describe METAL’s genome-wide association meta-analysis software, not CSF collection. Searches for standardized CSF collection and cerebrospinal fluid collection protocols found no relevant passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
May combine two claims.
Findingown datars7867518 near VLDLR showed the most significant association with CSF total tau but did not pass Bonferroni threshold.
“a SNP adjacent to VLDLR, rs7867518, showed the most significant association with CSF total tau (P=3.02×10−3), but failed to pass the Bonferroni-corrected threshold.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe protective rs1057233G allele is associated with higher CSF Aβ42 levels, and the risk rs1057233A allele with lower CSF Aβ42 levels.
“the protective rs1057233G allele was associated with higher CSF Aβ42 levels and the risk rs1057233A allele was associated with lower CSF Aβ42 levels.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAt all suggestive loci, AD risk-increasing alleles were associated with hazard ratio above 1 and earlier AAO, while risk-decreasing alleles were associated with hazard ratio below 1 and later AAO.
“The direction of effects were concordant with the previous IGAP GWAS logistic regression analysis for AD risk1 at all suggestive loci: AD risk-increasing alleles were all associated with a hazard ratio above 1 and earlier AAO, whereas AD risk-decreasing alleles were all associated with a hazard ratio below 1 and later AAO”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe rs1057233G allele is associated with lower MYBPC3 expression in monocytes and macrophages.
“This allele was also associated with lower expression of MYBPC3 (monocytes: P=5.58×10−23; macrophages: P=4.99×10−51)”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataGTEx analysis identified 50 unique associated SNP-gene pairs across 44 tissues, including 11 in brain regions.
“Further analysis using the GTEx dataset24 identified 50 unique, associated snp-gene pairs across 44 tissues, including 11 snp-gene pairs in various brain regions”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSchizophrenia heritability is not enriched in hematopoietic cells but is significantly enriched in brain.
“In contrast schizophrenia (SCZ) heritability was not enriched in hematopoietic cells (1.24 fold enrichment, P=0.53, as measured by summary statistics from the Psychiatric Genomics Consortium [PGC] GWAS26) but was significantly enriched in brain (18.61 fold enrichment, P=1.38×10−4 in fetal brain promoters/H3K4me3”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataThe genome-wide survival analysis of 14,406 AD cases and 25,849 controls identified eight previously reported AD risk loci associated with age at onset.
“A genome-wide survival analysis of 14,406 Alzheimer’s disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and fourteen novel loci associated with age at onset.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown datars1057233 and tagging SNPs are associated with AD risk and survival, and CSF Aβ42.
“Overall, rs1057233 and tagging SNPs are associated with AD risk and survival, and CSF Aβ42.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMS4A4A and MS4A6E showed evidence of colocalization only in monocytes, while SELL did not show evidence of colocalization.
“MS4A4A and MS4A6E in the MS4A locus showed evidence of co-localization only in monocytes, while SELL did not show evidence of colocalization.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPU.1 is bound to cis-regulatory elements of many AD-associated genes, including ABCA7, CD33, MS4A4A, MS4A6A, PILRA, PILRB, TREM2, TREML2, and TYROBP, as well as SPI1 itself, but notably not APOE.
“Further examination of PU.1 binding peaks and chromatin marks/states in human monocytes confirmed that PU.1 is bound to cis-regulatory elements of many AD-associated genes, including ABCA7, CD33, MS4A4A, MS4A6A, PILRA, PILRB, TREM2, TREML2, and TYROBP (as well as SPI1 itself, but notably not APOE)”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenMYBPC3/Mybpc3 is either not expressed or expressed at low levels in human and mouse microglia.
“However, MYBPC3/Mybpc3 (a myosin binding protein expressed at high levels in cardiac muscle cells) is either not expressed or expressed at low levels in human and mouse microglia, respectively.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSPI1 is the strongest and most consistent colocalization target at the SPI1/CELF1 locus, and the only gene where AD survival and gene expression association signals are likely driven by the same causal genetic variant in both monocytes and macrophages.
“These analyses (Supplementary Table 8) highlighted SPI1 at the SPI1/CELF1 locus as the strongest and most consistent colocalization target, and the only gene where the AD survival and gene expression association signals are likely (posterior probability ≥ 0.8) driven by the same causal genetic variant, in both monocytes and macrophages (PP.H4.abf of 0.85 and 0.83, respectively).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the survival analysis, rs10838725 showed weak association whereas rs1057233 showed the strongest association at the SPI1/CELF1 locus.
“In our survival analysis, however, rs10838725 showed weak association (P=0.12, HR=1.02, 95% CI=0.99–1.05) whereas rs1057233, located in the 3′UTR of a neighboring gene, SPI1, showed the strongest association (Table 1, P=5.4×10−6).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown datars1057233 remained significantly associated with AD after adjusting for rs10838725, whereas rs10838725 showed no evidence of association after adjusting for rs1057233.
“we found that rs1057233 remained significantly associated with AD after adjusting for rs10838725 (P=3.2×10−4), whereas rs10838725 showed no evidence of association after adjusting for rs1057233 (P=0.66).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe most significantly AAOS-associated SNP at the MS4A locus, rs7930318, shows a protective effect (HR = 0.93, 95% CI = 0.90-.95), consistent with the previous IGAP GWAS logistic regression analysis for AD risk (OR = 0.90, 95% CI = 0.87-.93).
“The most significantly AAOS-associated SNP at this locus, rs7930318, shows a protective effect (HR = 0.93, 95% CI = 0.90–.95) in the survival analysis, consistent with the previous IGAP GWAS logistic regression analysis for AD risk (OR = 0.90, 95% CI = 0.87–.93).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown datars7867518 at the VLDLR locus shows suggestive associations with both AAOS and CSF tau.
“In particular, rs7867518 at the VLDLR locus shows suggestive associations with both AAOS (P=9.1×10−6) and CSF tau (P=3.03×10−3).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMYBPC3 and MS4A6A also showed evidence of colocalization in both myeloid cell types, albeit not surviving posterior probability cutoff in one.
“MYBPC3 in the SPI1/CELF1 locus and MS4A6A in the MS4A locus also showed evidence of colocalization in both myeloid cell types albeit not surviving posterior probability cutoff in one of them.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataKnock-down of PU.1 expression reduced engulfment of zymosan, whereas overexpression increased engulfment.
“Knock-down of PU.1 expression reduced engulfment of zymosan, whereas overexpression of PU.1 increased engulfment of zymosan, a Toll-like receptor 2 (TLR2) agonist that mimics fungal pathogens.”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Zymosan phagocytosis is a valid proxy for microglial phagocytic activity relevant to AD pathogenesis.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown datars1057233 is associated with MYBPC3 expression in the replication dataset.
“We found strong evidence for the association between rs1057233 and SPI1 expression (P=6.39×10−102) as well as MYBPC3 expression (P=5.95×10−33”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAD heritability is significantly enriched in the PU.1 cistrome in both monocytes and macrophages, but not SCZ heritability.
“We found a significant enrichment of AD heritability in both monocytes (47.58 fold enrichment, P=6.94×10−3) and macrophages (53.88 fold enrichment, P=1.65×10−3), but not SCZ heritability”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe minor allele of all these SNPs showed protective effects in AD risk and survival analyses and lower expression of associated genes.
“Interestingly, the minor allele of all of these SNPs showed protective effects in both AD risk and survival analyses, as well as lower expression of the associated genes.”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationAmong 112 AD-associated genes, 75 had evidence of expression in human brain microglial cells, and 60 of those also had evidence of association with PU.1 binding sites in human blood myeloid cells.
“Among these genes, 75 had evidence of gene expression in human brain microglial cells, 60 of which also had evidence of association with PU.1 binding sites in human blood myeloid cells35”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [35] Steinberg, S. et al. Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease. Nat. Genet. 47, 445–447 (2015). source
May combine two claims.
Findingown dataThe protective allele at the MS4A locus is associated with lower expression of MS4A4A and MS4A6A in human myeloid cells, and lower Spi1 expression in BV2 cells led to lower expression of Ms4a4a and Ms4a6d.
“Our genetic analyses show that the protective allele at the MS4A locus is associated with lower expression of MS4A4A and MS4A6A in human myeloid cells, and the BV2 experiment demonstrated that lower expression of Spi1 (which is protective in humans) led to lower expression of Ms4a4a and Ms4a6d (mouse ortholog of MS4A6A), which are also associated with reduced AD risk in humans.”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Mouse orthologs of human AD risk genes have conserved regulation by Spi1.
May combine two claims.
Findingown dataPhagocytic activity is augmented in BV2 cells overexpressing PU.1, while knock-down of PU.1 results in decreased phagocytic activity.
“Analysis of zymosan uptake by flow cytometry revealed that phagocytic activity is augmented in BV2 cells overexpressing PU.1 (Fig. 3a), while knock-down of PU.1 resulted in decreased phagocytic activity (Fig. 3a).”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Zymosan phagocytosis is a valid proxy for microglial phagocytic activity relevant to AD pathogenesis.
May combine two claims.
Findingown dataAfter knock-down of Spi1 in BV2 cells, expression of Cd33, Tyrobp, Ms4a4a, and Ms4a6d decreased, and expression of Apoe and Clu/ApoJ increased.
“After knock-down of Spi1 in BV2 cells, expression of Cd33, Tyrobp, Ms4a4a and Ms4a6d decreased and expression of Apoe and Clu/ApoJ increased (Fig. 4a, 4c).”
Quote in the paperSubstantial claim- Rests on an assumption: Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
- Rests on an assumption: Mouse orthologs of human AD risk genes have conserved regulation by Spi1.
May combine two claims.
FindingcitationThe minor allele of rs1057233 (G) is suggestively associated with lower AD risk, later AAO, and significantly associated with higher CSF Aβ42.
“Notably, the minor allele of rs1057233 (G) is suggestively associated with lower AD risk (P=5.4×10−6, 5.9×10−7 in IGAP stage I, stage I and II combined, respectively)1, later AAO (P=8.4×10−6) and significantly associated with higher CSF Aβ42 (P=4.11×10−4)”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataJoint analyses and meta-analyses yielded no major differences.
“No major differences were found between the joint-analyses and the meta-analyses.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataKnockdown of Spi1 increased Il34 and Csf1 expression and decreased Csf1r expression.
“expression of Il34 and Csf1, soluble factors that bind to Csf1r and required for microglial development and maintenance in vivo49, were elevated after knock-down of Spi1, while expression of Csf1r was reduced (Fig. 4a, 4c).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Findingown dataAmong the 751 suggestive SNPs (P < 1×10−5), standard errors and confidence intervals were lower as the number of cohorts showing consistent directionality of effect increased.
“Of the 751 suggestive SNPs (P < 1×10−5), we found these SNPs to show lower standard errors and confidence intervals with the increasing number of cohorts showing consistent directionalities of effect.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe average standard error for SNPs showing 1 to 7 consistent directionalities ranged from 0.171 to 0.01795.
“the average standard error for SNPs showing 1 to 7 consistent directionalities ranges from 0.171, 0.109, 0.0744, 0.0346, 0.0234, 0.0173 to 0.01795 (Supplementary Fig. 1b).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationSeveral AD-associated genes are involved in phagocytic clearance of pathogens or host-derived cellular material, suggesting a strong link between perturbation of microglial phagocytosis and AD pathogenesis.
“Interestingly, several AD-associated genes (e.g., CD33, TYROBP, TREM2, TREML2, CR1, ABCA7, APOE, CLU/APOJ) have been shown to be involved in the phagocytic clearance of pathogens or host-derived cellular material (e.g., β-amyloid, apoptotic cells, myelin debris, lipoproteins, etc.), suggesting a strong link between perturbation of microglial phagocytosis and AD pathogenesis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataLow MYBPC3 expression in human myeloid cells is possibly due to leaky transcription driven by the adjacent highly expressed SPI1 gene.
“suggesting that low levels of MYBPC3 expression in human myeloid cells are possibly due to leaky transcription driven by the adjacent highly expressed SPI1 gene.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPU.1 expression is dynamically and tightly controlled during haematopoiesis to direct specification of CD34+ hematopoietic stem and progenitor cells toward myeloid and B-lymphoid lineage.
“In particular, PU.1 expression is dynamically and tightly controlled during haematopoiesis to direct the specification of CD34+ hematopoietic stem and progenitor cells toward the myeloid and B-lymphoid lineage by progressively partitioning into CD14+ monocytes/macrophages, CD15+ neutrophils, and CD19+ B cells46”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [46] Smith, A.M. et al. The transcription factor PU.1 is critical for viability and function of human brain microglia. Glia 61, 929–942 (2013). source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe VLDLR-5-repeat allele was found to be associated with dementia.
“Additionally, the VLDLR-5-repeat allele was found to be associated with dementia38.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [38] Bajari, T.M., Strasser, V., Nimpf, J. & Schneider, W.J. A model for modulation of leptin activity by association with clusterin. FASEB J. 17, 1505–1507 (2003). source
May combine two claims.
Interpretationown datars1057233 and its tagging SNPs likely represent the underlying disease locus and may modulate AD risk through variation in SPI1 expression.
“Thus, rs1057233 and its tagging SNPs likely represent the underlying disease locus and may modulate AD risk through variation in SPI1 expression.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe western blotting used two independent experiments, without randomization of sample processing, and the researcher was not blinded to sample identity.
“Two independent experiments were performed without randomization of sample processing, n = 2. Researcher was not blinded to the samples identification.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataEvery AD case satisfied criteria for probable AD by NINCDS-ADRDA or DSM-IV, or definite AD by CERAD.
“All AD cases met criteria for either probable (NINCDS-ADRDA, DSM-IV) or definite (CERAD) AD.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenControls were assessed with the same criteria and had no cognitive impairment symptoms.
“Control individuals were evaluated using the same criteria and showed no symptoms of cognitive impairment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenAll studies obtained written consent from participants or their proxies, and their protocols received approval from the relevant institutional review boards.
“For all studies, written informed consent was obtained from study participants or, for those with substantial cognitive impairment, from a caregiver, legal guardian, or other proxy, and the study protocols for all populations were reviewed and approved by the appropriate Institutional review boards.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataGenomic inflation was low (λ = 1.026).
“which showed little evidence of genomic inflation (λ = 1.026).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenSex, site and the first three principal components were included in all regression models to control for their effects.
“We incorporated sex, site and the first three principal components from EIGENSTRAT30 in all our regression models to control for their effects.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataAt a minor allele frequency of 0.15, power was about 80% for odds ratios above 1.23 or below 0.81; at 0.3, about 80% for odds ratios above 1.18 or below 0.85.
“For variants with a MAF of 0.15, we would have approximately 80% power to detect effects for OR > 1.23 or < 0.81; for variants with a MAF of 0.3, we would have approximately 80% power to detect effects for OR > 1.18 (or < 0.85).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAlpha was adjusted to 5×10−8.
“Alpha was adjusted to 5×10−8.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataRoutine testing showed that the BV2 cell line was negative for mycoplasma contamination.
“Routine testing of cell lines using MycoAlert PLUS mycoplasma detection kit (Lonza) showed that BV2 cells were negative for mycoplasma contamination.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenElderly controls underwent dementia screening with MMSE or ADAS-cog and were either free of dementia on neuropathology or had a Braak score no higher than 2.5.
“All elderly controls were screened for dementia using the MMSE or ADAS-cog, were determined to be free from dementia at neuropathological examination or had a Braak score of 2.5 or lower.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe imputed GERAD data were analyzed by logistic regression with covariates for country, sex, age, and three principal components.
“The imputed data was then analysed using logistic regression including covariates for country of origin, gender, age and 3 principal components obtained with EIGENSTRAT software based on individual genotypes for the GERAD study participants.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPrior studies have shown that the relationship between CSF Aβ42 levels and age differs between individuals under 45 years and those over 45 years, which is why individuals under 45 were removed from the CSF analysis.
“individuals under age 45 years were removed because prior studies have demonstrated that the relationship between CSF Aβ42 levels and age appears to differ in individuals below 45 years vs. those above 45 years69.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [69] Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007). source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenEvery participant gave written informed consent, and the ethics committee approved the consent procedure.
“All participants provided written informed consent and the ethics committee approved the informed consent procedure (IRB ID #: 201105364).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe null distribution of the Wald test P values for the eQTL analysis was estimated by rerunning the analysis on a permuted dataset in which expression sample identifiers were permuted.
“The distribution of the Wald test P values under the null hypothesis of no correlation between genotype and gene expression was estimated by rerunning the same analysis on a null dataset obtained by permuting the expression samples identifiers.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataThe proportional hazards assumption of the Cox model was validated by the Schoenfeld test in the 22 prioritized SNPs, and none of the SNPs had a Schoenfeld P value below 0.035 in any of the 7 cohorts.
“We validated the assumption of proportional hazards assumed by the Cox model by conducting the Schoenfeld test in the 22 prioritized SNPs. None of the SNPs has a Schoenfeld P value, which is the P value for Pearson product-moment correlation between the scaled Schoenfeld residuals and time, lower than 0.035 (multiple test correction threshold = 0.00227) in any of the 7 cohorts.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The Cox model's proportional hazards assumption holds.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe quantitative PCR used four independent experiments, without randomization of sample processing, and the researcher was not blinded to sample identity.
“Four independent experiments were performed without randomization of sample processing, n = 4. Researcher was not blinded to the sample identity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenPotentially related individuals were excluded so that all remaining individuals had a kinship coefficient below 0.05.
“We excluded potentially related individuals so that all remaining individuals have kinship coefficient below 0.05.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenIn the ADGC, GERAD, CHARGE and CSF datasets, SNPs that failed Hardy-Weinberg equilibrium in controls at a P value threshold of 1×10−6 were removed.
“In the ADGC, GERAD, CHARGE, and CSF datasets, we then removed SNPs that failed the Hardy-Weinberg equilibrium in controls calculated based on the imputed best-guess genotypes using a P value threshold of 1×10−6.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenWithin each study, biomarker assays used internal standards and controls to ensure consistency and reliability.
“Biomarker measurements within each study were conducted using internal standards and controls to achieve consistency and reliability.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn a 3xTg-AD mouse model, blocking Csf1r lowered the count of microglia linked to β-amyloid plaques and enhanced cognition.
“inhibition of Csf1r in a 3xTg-AD mouse model led to a reduction in the number of microglia associated with β-amyloid plaques and improved cognition50.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [50] Alpérovitch, A. et al.; 3C Study Group. Vascular factors and risk of dementia: design of the Three-City Study and baseline characteristics of the study population. Neuroepidemiology 22, 316–325 (2003). source
May combine two claims.
Interpretationown dataLower Spi1 expression might reduce the pro-inflammatory reaction of microglial cells and thereby improve disease outcomes.
“suggesting that decreased Spi1 expression may blunt the pro-inflammatory response of microglial cells to improve disease outcomes.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenIndividuals with more than 5% missing genotype rates, a discrepancy between reported and genetically estimated sex, or evidence of non-European ancestry based on principal component analysis were excluded.
“we excluded individuals who had > 5% missing genotype rates, who showed a discrepancy between reported sex and sex estimated on the basis of genetic data, or who showed evidence of non-European ancestry based on principal component analysis using PLINK1.970.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe phagocytosis assay used three independent experiments with two technical replicates, without randomization of sample processing, and the researcher was not blinded to sample identity.
“Three independent experiments were performed with two technical replicates without randomization of sample processing, n = 3. Researcher was not blinded to the samples identification.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataAfter quality control, the imputed ADGC sample included 8,617 AD cases and 9,765 controls.
“The imputed ADGC sample that passed quality control procedures comprised of 8,617 AD cases and 9,765 control subjects”
Quote in the paperSubstantial claim
The paper relies on this without support.
Mouse BV2 microglial cells are a valid model for human microglial function relevant to AD.
“We modulated levels of PU.1 by Spi1 cDNA overexpression or shRNA knock-down in BV2 mouse microglial cells”
“To validate these bioinformatic analyses, we show that experimentally altered PU.1 levels are correlated with phagocytic activity of mouse microglial cells”
The paper relies on this without support.
Cis-eQTL effects observed in monocytes and macrophages reflect regulation in brain microglia relevant to AD.
“we analyzed cis-eQTL effects of the AAOS-associated SNPs and their tagging SNPs in human cis-eQTL datasets composed of 738 monocyte and 593 macrophage samples from the Cardiogenics consortium27”
“SPI1, MS4A4A, MS4A6A, and SELL are specifically expressed in microglia based on RNA-Seq data29–31”
The paper relies on this without support.
Zymosan phagocytosis is a valid proxy for microglial phagocytic activity relevant to AD pathogenesis.
“used zymosan bioparticles labeled with pHrodo (a pH-sensitive dye that emits a fluorescent signal when internalized in acidic vesicles during phagocytosis) to measure pathogen engulfment”
“zymosan, a Toll-like receptor 2 (TLR2) agonist that mimics fungal pathogens”
The paper relies on this without support.
The IGAP GWAS summary statistics and Cardiogenics eQTL datasets are from comparable populations and suitable for integration.
“we used 14,406 AD case and 25,849 control samples from the IGAP consortium”
“human cis-eQTL datasets composed of 738 monocyte and 593 macrophage samples from the Cardiogenics consortium27”
The paper relies on this without support.
Mouse orthologs of human AD risk genes have conserved regulation by Spi1.
“expression of mouse orthologs of many AD risk genes”
“Ms4a6d (mouse ortholog of MS4A6A)”
The paper relies on this without support.
Ccl2 and Cxcl2 correspond to MCP1 and MIP2α, proteins that draw circulating monocytes and neutrophils into the brain and drive neuroinflammation.
“which are MCP1 and MIP2α proteins that help recruitcirculating monocytes and neutrophils to the brain to promote neuroinflammation.”
The paper relies on this without support.
SPI1 codes for PU.1, a transcription factor present in microglia and other myeloid cells that directly controls other AD-associated genes in those cells.
“SPI1 encodes PU.1, a transcription factor expressed in microglia and other myeloid cells that directly regulates other AD-associated genes expressed in these cell types.”
The authors state this as an assumption.
The Cox model's log-linearity assumption holds.
“The model assumes log-linearity and proportional hazards.”
The authors state this as an assumption.
The Cox model's proportional hazards assumption holds.
“The model assumes log-linearity and proportional hazards.”
The authors state this as an assumption.
The effect of SNP dosage on the hazard is additive.
“We conducted a genome-wide Cox proportional hazards regression72 assuming an additive effect from SNP dosage.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Mybpc3 upregulation serves as a positive indicator of Spi1 downregulation.
“which serves as a positive indicator of Spi1 downregulation.”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [24]
The Results section on cis-eQTL associations reports a positive correlation between MYBPC3 and SPI1 expression in monocytes and macrophages. It also reports that the same allele is associated with lower expression of both genes. Thus, the paper does not support MYBPC3 upregulation as an indicator of SPI1 downregulation; its reported association runs in the opposite direction. Page numbers are unavailable in our copy. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe rs1057233 SNP in SPI1 is a protective allele (OR=0.93, 95% CI=0.91-0.97) associated with lower SPI1 mRNA levels in peripheral immune cells.
“This study identified a specific variant within the SPI1 gene (rs1057233 SNP) as a protective allele (odds ratio [OR] = 0.93, 95% confidence interval [CI] = 0.91–0.97) and found it is associated with lower SPI1 mRNA levels in peripheral immune cells (monocytes and macrophages)7.”
From Deletion of SPI1 in microglia exacerbates amyloid pathology by impairing microglial response in Alzheimer’s disease models (2026) · cites this paper as [7]
The Results section (Cis-eQTL associations and colocalization analysis; page unavailable) identifies the protective rs1057233G allele and reports its association with lower SPI1 expression in monocytes and macrophages. The passages retrieved for the stated odds ratio and confidence interval do not establish OR = 0.93 or 95% CI = 0.91–0.97, so those figures are not verified by our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAD SNP heritability showed its strongest enrichment in microglial regulatory elements, especially microglial enhancers.
“In contrast, AD SNP heritability was most highly enriched in microglia regulatory elements (Fig. 1D), specifically microglia enhancers (18-23).”
From Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association (2019) · cites this paper as [20]
The Results section (page not available) reports enrichment of AD heritability in myeloid and B-lymphoid cells, including monocyte enhancers. It does not report that microglial regulatory elements were tested or that microglial enhancers showed the strongest enrichment; the Discussion instead calls myeloid cells the top candidate cell types. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSPI1/PU.1 is a master regulator of microglial development and homeostasis and of several Alzheimer's disease genetic risk genes, and small changes in PU.1 expression can significantly affect cell function.
“SPI1/PU.1 is a master regulator of microglial development and homeostasis and the expression of several AD genetic risk genes, e.g. ABCA7, CD33, MS4A4A, MS4A6A, TREM2, APOE and CLU, and small changes in PU.1 expression can significantly affect cell function (Huang et al., 2017; Satoh et al., 2014).”
From Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response (2021) · cites this paper as Huang et al., 2017
The Results section on the PU.1 cistrome reports binding at several AD-associated genes, and its functional experiments show that altering PU.1 changes gene expression and phagocytosis in mouse microglial cells. The Discussion describes PU.1 as required for myeloid-cell development and function, but the paper does not establish that *small* expression changes significantly affect cell function or specifically characterize PU.1 as a master regulator of microglial homeostasis. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe SPI1 locus is one of the few AD risk loci for which a mechanism of action based on modulation of gene expression by common disease risk alleles has been delineated.
“The SPI1 locus is one of the few AD risk loci for which a mechanism of action based on the modulation of gene expression by common disease risk alleles has been delineated (Huang et al., 2017).”
From Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response (2021) · cites this paper as Huang et al., 2017
The Results (Cis-eQTL associations and colocalization analysis; page unavailable) link a protective allele to lower SPI1 expression in myeloid cells, and the Discussion proposes expression changes as a route by which the locus may affect AD risk. But the Discussion calls this a hypothesis, and the Results (Conditional and SMR analysis; page unavailable) say the functional variant has not been definitively identified. Saying the mechanism has been delineated is therefore stronger than the paper's conclusion. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Studies investigating PU.1 in human microglia suggest there are shared Spi1/PU.1 dose dependent pathways in microglia.
“However, studies investigating PU.1 in human microglia suggest there are shared Spi1/PU.1 dose dependent pathways in microglia.”
From Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease (2021) · cites this paper as [7]
The Discussion (page unavailable) says that PU.1 knock-down reduced phagocytosis in mouse microglial cells, consistent with a reported reduction in Aβ42 uptake after SPI1 silencing in human microglia. This supports a suggested shared response, but the human result concerns knock-down only; the paper does not establish multiple dose-dependent pathways in human microglia. (LLM, conf=0.78)
Experimental — accuracy not yet measuredA common variant located within the 3′ untranslated region of the PU.1-encoding gene SPI1, associated with reduced PU.1 expression in myeloid cells, correlates with delayed disease onset and reduced severity.
“Human genetic data show that a common variant, located within the 3′ untranslated region of the PU.1-encoding gene SPI1 and associated with reduced PU.1 expression in myeloid cells, correlates with delayed disease onset and reduced severity24.”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [24]
The Abstract reports that the rs1057233 G allele is associated with lower SPI1 expression in monocytes and macrophages and delayed Alzheimer’s disease onset. The Results (Conditional and SMR analysis; Cerebrospinal fluid biomarkers associations) also link the allele to AD risk and a CSF biomarker, but do not establish reduced disease severity. The passages found do not specify that rs1057233 lies in SPI1’s 3′ untranslated region. (LLM, conf=0.85)
Experimental — accuracy not yet measuredGene expression analysis from large cohorts of human microglia revealed that individuals carrying the PU.1-lowering protective allele (GG/GA versus AA) have an increased abundance of the lymphoid gene-expressing microglia subset (Mic. 13).
“A gene expression analysis from large cohorts of human microglia17 revealed that individuals carrying the PU.1-lowering protective allele (GG/GA versus AA), which was previously associated with improved cognitive performance and reduced AD severity24, have an increased abundance of the lymphoid gene-expressing microglia subset (Mic. 13; Fig. 5f,g).”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [24]
The Results (Cis-eQTL associations and colocalization analysis) identify rs1057233G as a protective allele associated with lower SPI1/PU.1 expression in human monocytes and macrophages. The paper also associates it with later Alzheimer’s onset and a favorable CSF biomarker, but does not establish improved cognitive performance or reduced disease severity. The Mic. 13 abundance finding is not attributed to this source by citation 24. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe PU.1-lowering protective allele (GG/GA versus AA) was previously associated with improved cognitive performance and reduced Alzheimer's disease severity.
“which was previously associated with improved cognitive performance and reduced AD severity24”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [24]
The Results (Genome-wide survival analysis and Cerebrospinal fluid biomarkers associations; pages unavailable) associate the protective rs1057233 G allele with later Alzheimer’s onset, lower AD risk, and a favorable CSF biomarker level. They do not establish that GG/GA carriers had better cognitive performance than AA carriers or less severe Alzheimer’s disease; the cognitive tests described in Online Methods were used for assessment, not reported as genotype-specific outcomes. (LLM, conf=0.86)
Experimental — accuracy not yet measuredUpon removing one allele of the Spi1 gene, the non-microglia-expressed, co-regulated and adjacent Mybpc3 gene was upregulated.
“Instead, upon removing one allele of the Spi1 gene, the non-microglia-expressed, co-regulated and adjacent Mybpc3 gene24 was upregulated”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [24]
The Results section on cis-eQTL associations describes MYBPC3 as adjacent to SPI1, correlated with its expression, and absent or expressed at low levels in microglia. It does not report removal of a Spi1 allele or subsequent Mybpc3 upregulation; instead, an allele associated with lower SPI1 expression was also associated with lower MYBPC3 expression. The functional-analysis Results describe Spi1 knock-down, but not that claimed Mybpc3 response. (LLM, conf=0.88)
Experimental — accuracy not yet measuredDownregulation of Spi1 in the BV2 microglial cell line decreased the expression of proinflammatory genes while increasing the transcription of genes essential in lipid metabolism.
“Earlier studies demonstrated that downregulation of Spi1 in the BV2 microglial cell line decreased the expression of proinflammatory genes while increasing the transcription of genes essential in lipid metabolism1.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
The Results section reports that knocking down Spi1 in BV2 cells increased Apoe and Clu/ApoJ expression. The Discussion reports reduced expression of several proinflammatory genes. The paper links Apoe and Clu/ApoJ to clearance of material including lipoproteins, but does not establish the stronger claim that the increased genes are essential to lipid metabolism. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSpi1-knockdown decreases microglial phagocytic activity in vitro, which may lead to an increase in the levels of toxic protein aggregates and dead cell debris.
“On the contrary, Spi1-knockdown also decreases microglial phagocytic activity in vitro, which may lead to an increase in the levels of toxic protein aggregates and dead cell debris1,7.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
The Results section reports that Spi1/PU.1 knockdown decreased phagocytic activity in cultured BV2 mouse microglial cells, measured by uptake of zymosan. The Discussion links microglial phagocytosis to clearance of β-amyloid and cellular debris, but does not show or specifically predict that knockdown increases their levels. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe association of PICALM polymorphisms with late-onset AD has been reported by the majority of GWAS studies.
“The association of PICALM polymorphisms with late-onset AD has been reported by the majority of GWAS studies (94, 119, 236, 265, 298, 344, 528).”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [265]
The Discussion reports that PICALM was previously associated with AD risk, and the Results find a genome-wide significant PICALM association with age at onset. Neither passage establishes that a majority of GWAS studies reported the association; the paper does not quantify that proportion. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIt is unclear whether SPI1 genetic factors modulate Alzheimer's disease risk in populations of non-European descent, because related studies have only been conducted in populations of European descent.
“It is unclear whether SPI1 genetic factors modulate AD risk in populations of non-European descent, as related studies have only been conducted in populations of European descent.”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [5]
Online Methods > Quality Control (page unavailable) states that individuals with evidence of non-European ancestry were excluded from this paper’s analyses. That supports uncertainty about whether its SPI1 findings apply to non-European populations, but the paper does not establish that all related studies were conducted only in European-descent populations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThree SPI1 single-nucleotide polymorphisms-rs3740688, rs1057233, and rs78245530-protect against Alzheimer's disease risk in populations of European descent.
“three single-nucleotide polymorphisms (SNPs) of SPI1—rs3740688, rs1057233, and rs78245530—protect against AD risk in populations of European descent [4–6].”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [5]
The Results section (‘Conditional and SMR analysis of the SPI1/CELF1 locus’; page unavailable) reports an association between rs1057233 and Alzheimer’s disease risk, while the CSF biomarker results call its G allele protective. Searches for rs3740688 and rs78245530 found no passages addressing those SNPs, so this paper does not support the claim for all three. It also does not establish that rs1057233 itself causally protects against disease. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSPI1 has been reported to be a genetic risk factor for Alzheimer's disease.
“has been reported to be a genetic risk factor for AD”
From Deletion of SPI1 in microglia exacerbates amyloid pathology by impairing microglial response in Alzheimer’s disease models (2026) · cites this paper as [7]
The transcription factor PU.1, encoded by SPI1, is expressed mainly in microglia in the central nervous system.
“The transcription factor PU.1 (encoded by SPI1) is expressed mainly in microglia in the central nervous system”
From Deletion of SPI1 in microglia exacerbates amyloid pathology by impairing microglial response in Alzheimer’s disease models (2026) · cites this paper as [7]
A human genetic study reported that lower SPI1 expression is associated with delayed onset of AD.
“Interestingly, our findings appear to contradict a human genetic study that reported the association of lower SPI1 expression with the delayed onset of AD7.”
From Deletion of SPI1 in microglia exacerbates amyloid pathology by impairing microglial response in Alzheimer’s disease models (2026) · cites this paper as [7]
Spi1 overexpression enhances microglial phagocytosis, whereas Spi1 knockdown reduces this function in the same cell line.
“Conversely, Spi1 overexpression enhances microglial phagocytosis, whereas its knockdown reduced this function in the same cell line7,25.”
From Deletion of SPI1 in microglia exacerbates amyloid pathology by impairing microglial response in Alzheimer’s disease models (2026) · cites this paper as [7]
Increased PU.1 expression levels are associated with Alzheimer's disease and Huntington's disease.
“Increased PU.1 expression levels are associated with AD (Gjoneska et al., 2015; Huang et al., 2017; Rustenhoven et al., 2018) and Huntington’s disease (Crotti et al., 2014)”
From A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation (2023) · cites this paper as Huang et al., 2017
Polymorphisms in the PU.1 gene SPI1 that reduce PU.1 expression levels lower the risk of Alzheimer's disease and delay Alzheimer's disease onset.
“while polymorphisms in the PU.1 gene SPI1 that reduce PU.1 expression levels lower the risk of AD (Cao et al., 2022) and delay AD onset (Huang et al., 2017).”
From A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation (2023) · cites this paper as Huang et al., 2017
Reduced PU.1 expression is protective against AD.
“Given that reduced PU.1 expression is protective against AD (Cao et al., 2022; Huang et al., 2017)”
From A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation (2023) · cites this paper as Huang et al., 2017
Lower PU.1 expression is protective against Alzheimer's disease, whereas higher expression is associated with increased risk.
“Higher expression of SPI1 is associated with increased risk for AD, while lower expression is protective.”
From Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response (2021) · cites this paper as Huang et al., 2017
SPI1 is the most likely gene mediating the association of the CELF1 locus with Alzheimer's disease risk.
“We have previously reported that SPI1 is the most likely gene mediating the association of the CELF1 locus with AD risk (Huang et al., 2017).”
From Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response (2021) · cites this paper as Huang et al., 2017
PU.1 regulates zymosan phagocytosis.
“We have previously reported that PU.1 regulates zymosan phagocytosis (Huang et al., 2017) and others reported that PU.1 knock-down reduces Aβ42 uptake in human microglial cells (Smith et al., 2013).”
From Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response (2021) · cites this paper as Huang et al., 2017
In the transcriptomic comparison, both DAM and PU.1 overexpression upregulated TREM1 pathways, whereas PU.1 knock-down showed decreased activation of those pathways.
“Interestingly, DAM and PU.1 overexpression both upregulated TREM1 pathways, while PU.1 knock-down showed decreased activation, which aligns with reduced uptake capacity in BV2 cells after PU.1 downregulation (Huang et al., 2017).”
From Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response (2021) · cites this paper as Huang et al., 2017
Variants denoting higher SPI1 expression are likely to have an earlier Alzheimer's disease onset.
“Reports linking risk variants to gene expression suggest that variants denoting higher SPI1 expression are likely to have an earlier AD onset”
From Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease (2021) · cites this paper as [7]
Multiple variants at the SPI1 locus modify risk and age of onset for Alzheimer's disease.
“Genetic association studies have identified multiple variants at the SPI1 locus that modify risk and age of onset for Alzheimer’s Disease (AD).”
From Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease (2021) · cites this paper as [7]
SPI1 binding sites are located near other AD risk genes or regulatory elements.
“In addition to SPI1 binding sites being located near other AD risk genes or regulatory elements5,7,8, SPI1 risk alleles associated with a higher gene expression have been shown to lower the age of onset in AD7.”
From Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease (2021) · cites this paper as [7]
Previous work has identified that Spi1 is able to influence multiple gene networks in microglia and has been proposed as central to a network of AD risk genes conserved between species.
“Previous work has identified that Spi1 is able to influence multiple gene networks in microglia5,7,31 and has been proposed as central to a network of AD risk genes that are conserved between species50.”
From Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease (2021) · cites this paper as [7]
In BV-2 cells, a reduction in PU.1 results in reduced Csf1r expression.
“In BV-2 cells a reduction in PU.1 also results in a reduced Csf1r expression7 though no cell number/viability data was provided.”
From Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease (2021) · cites this paper as [7]
SPI1 risk alleles associated with a higher gene expression lower the age of onset in AD.
“In addition to SPI1 binding sites being located near other AD risk genes or regulatory elements5,7,8, SPI1 risk alleles associated with a higher gene expression have been shown to lower the age of onset in AD7.”
From Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease (2021) · cites this paper as [7]
Human SNPs that altered SPI1 expression level are associated with Alzheimer's disease pathogenesis.
“human SNPs that altered SPI1 expression level were reported to be associated with Alzheimer’s disease (AD) pathogenesis (Cao et al., 2022; Huang et al., 2017).”
From Pu.1/Spi1 dosage controls the turnover and maintenance of microglia in zebrafish and mammals (2025) · cites this paper as Huang et al., 2017
In vitro cell culture studies have demonstrated that Spi1 regulates the expression of AD-associated genes involved in the phagocytic activity and immune response of microglia.
“In vitro cell culture studies have demonstrated that Spi1 regulates the expression of AD-associated genes involved in the phagocytic activity and immune response of microglia1,6.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
SPI1 was recently identified as a genetic risk factor for Alzheimer's disease in large-scale genome-wide association studies.
“Human genetic studies recently identified SPI1 (encoding PU.1) as a risk factor for Alzheimer’s disease (AD)1–3.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
SPI1 gene has been identified as a genetic risk factor in recent human AD genetic studies.
“SPI1 gene has been identified as a genetic risk factor in recent human AD genetic studies1–3.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
The SNP rs1057233 is known as a protective allele and was associated with lower SPI1 expression in myeloid cells.
“Among the genetic variants linked to AD at the SPI11 locus, the SNP rs1057233 is known as a protective allele and was associated with lower SPI1 expression in myeloid cells1.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
PU.1 can bind to regulatory elements of multiple AD-associated genes, including TYROBP, TREM2, CD33, MS4A4A, and ABCA7, and thereby regulate their expression.
“Importantly, chromatin immunoprecipitation sequencing analysis demonstrated that PU.1 can bind to regulatory elements of multiple AD-associated genes, including TYROBP, TREM2, CD33, MS4A4A, and ABCA7, and thereby regulate their expression1.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
The rs1057233 SNP is associated with reduced SPI1 mRNA levels in peripheral immune cells (monocytes and macrophages).
“The rs1057233 SNP is associated with reduced SPI1 mRNA levels in peripheral immune cells (monocytes and macrophages)1.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [1]
Genome-wide association studies suggest that SPI1 is associated with Alzheimer's disease.
“genome-wide association studies (GWASs) suggest that SPI1—which encodes PU.1, a transcription factor that regulates the expression of immune-related genes in myeloid cells [7], is associated with AD [4–6].”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [5]
rs1057233 tags a common haplotype that lowers SPI1 transcript level in myeloid cells.
“rs1057233 tags a common haplotype that lowers SPI1 transcript level in myeloid cells [5].”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [5]
Non-coding mutations modulate Alzheimer's disease risk, likely through modifying gene expression.
“non-coding mutations also modulate AD risk [4–6], likely through modifying gene expression.”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [5]
A common haplotype tagged by SNP rs1057233 (SPI1 haplotype β) protects against AD risk.
“In the SPI1 locus, a common haplotype tagged by SNP rs1057233 (denoted as SPI1 haplotype β in this study) was shown to protect against AD risk [5].”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [5]
Manipulation of SPI1 gene expression can change the gene expression of microglia and modify phagocytic activity.
“For example, manipulation of SPI1 gene expression can change the gene expression of microglia and modify phagocytic activity [5].”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [5]
Human genetic studies suggest that reduced SPI1 expression may be protective against Alzheimer's disease.
“Human genetic studies suggest that reduced SPI1 expression may be protective, whereas experimental data indicate that excessive PU.1 suppression can impair essential microglial functions.”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [29]
A common protective haplotype associated with lower SPI1 expression was reported to delay Alzheimer's disease onset, suggesting that reduced PU.1 abundance in myeloid cells may be beneficial under some disease-relevant conditions.
“A common protective haplotype associated with lower SPI1 expression was reported to delay Alzheimer’s disease onset, suggesting that reduced PU.1 abundance in myeloid cells may be beneficial under some disease-relevant conditions.”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [29]
Alzheimer's disease risk can be influenced not only by coding changes in immune genes, but also by regulatory variants that alter how strongly a myeloid transcription factor is expressed.
“The key implication was that Alzheimer’s risk could be influenced not only by coding changes in immune genes, but also by regulatory variants that alter how strongly a myeloid transcription factor is expressed.”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [29]
rs4942482 on chromosome 13 is near a variant previously associated with CSF clusterin and replicates in three studies and the ADGC EOAD study in progress.
“Finally, rs4942482 on chromosome 13 is near a variant previously associated with CSF clusterin [41] and replicates in three studies [17,19] and ADGC EOAD study in progress.”
From Genetic associations with age at dementia onset in the <i>PSEN1 E280A</i> Colombian kindred (2023) · cites this paper as [17]
The cited Huang et al. (2017) paper was not indexed for paper-scoped text search, so its discussion of rs4942482 could not be checked. Unscoped searches found no close match for the claim; the returned clusterin papers discuss Alzheimer’s disease biomarkers but not the specified variant and replications. There is no reliable evidence here to confirm the attribution or identify a different source. (LLM, conf=0.74)
Experimental — accuracy not yet measuredNo hematological disorders have been described in humans carrying the G allele at rs1057233, which lowers PU.1 expression levels approximately two-fold in circulating monocytes and macrophages.
“In addition, no hematological disorders have been described in humans carrying the G allele at rs1057233 (Huang et al., 2017) which lowers PU.1 expression levels approximately two-fold in circulating monocytes and macrophages.”
From A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation (2023) · cites this paper as Huang et al., 2017
The citation follows the claim that no hematological disorders have been described in people carrying the rs1057233 G allele. The Results (Cis-eQTL associations and colocalization analysis; page unavailable) associate that allele with lower SPI1 expression in monocytes and macrophages, but neither that section nor the Discussion passages found address whether carriers have hematological disorders. Searched for hematological and blood disorders in carriers. (LLM, conf=0.74)
Experimental — accuracy not yet measuredReduced PU.1 expression associates with delayed AD onset.
“GWAS studies suggest that reduced PU.1 expression associates with delayed AD onset [492].”
From Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease (2020) · cites this paper as [492]
PU.1 regulates expression of genes associated with AD risk or onset, including ABCA7, CD33, TREM2, MS4A4A, MS4A6A, TYROBP, Aif1, and MYBPC3.
“In addition, PU.1 can regulate expression of genes associated with AD risk or onset, including ABCA7, CD33, TREM2, MS4A4A, MS4A6A, TYROBP, Aif1, and MYBPC3 [492, 493].”
From Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease (2020) · cites this paper as [492]
An Alzheimer's disease risk locus initially attributed to CELF1 was later shown to affect expression of the myeloid lineage transcription factor SPI1/PU.1.
“For example, an Alzheimer’s Disease risk locus initially attributed to the CELF1 gene was later shown to affect expression of the myeloid lineage transcription factor SPI1/PU.14.”
From CRISPR-based functional genomics for neurological disease (2020) · cites this paper as [4]
Many risk genes for Alzheimer's disease are exclusively or highly expressed in myeloid cells.
“Many risk genes for the development of Alzheimer’s disease (AD) are exclusively or highly expressed in myeloid cells.”
From Sustained microglial depletion with CSF1R inhibitor impairs parenchymal plaque development in an Alzheimer’s disease model (2019) · cites this paper as [6]
Genes linked to early-onset AD risk that are expressed primarily in microglia include TREM2, MS4A, ABCA7, ApoE4, CD33, PU.1, and BIN1.
“Genetic studies show that a number of genes that have been linked to early-onset AD risk are expressed primarily in microglia.”
From Microglial signatures and their role in health and disease (2018) · cites this paper as [135]
Innate immunity contributes to Alzheimer disease through altered PU.1-driven transcriptional regulation in microglia and blood monocytes.
“This study implicates innate immunity in AD through alteration of PU.1 core transcriptional regulation of microglia and blood monocytes.”
From Microglial signatures and their role in health and disease (2018) · cites this paper as [135]
Most GWAS loci are thought to operate by regulating the expression of neighbouring genes.
“Since most GWAS loci are thought to operate by regulating the expression of neighbouring genes (Bradshaw et al., 2013; Griciuc et al., 2013; Huang et al., 2017)”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
A common SNP in the population delays onset of Alzheimer's disease, purportedly via reduced expression of SPI1/PU.1.
“The study by Huang et al. (2017) shows that a common SNP in the population delays onset of Alzheimer’s disease, purportedly via reduced expression of SPI1/PU.1.”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
A decade of GWAS projects for Alzheimer's disease has provided insights into the progression of late-onset Alzheimer's disease, particularly the dependence on the innate immune system, with the identification of genes such as TREM2 and SPI1/PU.1.
“A decade of GWAS projects for Alzheimer’s disease has provided key and initially surprising insights into the progression of late-onset Alzheimer’s disease, particularly the dependence on the innate immune system, with the identification of genes such as TREM2 and SPI1/PU.1”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
SPI1/PU.1 itself alters Alzheimer's disease risk by coordinating a program of microglial-expressed genes.
“which itself alters Alzheimer’s disease risk by coordinating a program of microglial-expressed genes (Huang et al., 2017)”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
SPI1/PU.1 was in the same ROS/MAP module as LAPTM5, ITGAM/CD11b and LILRB4.
“SPI1/PU.1, the myeloid cell transcription factor and a newly discovered GWAS risk gene (Huang et al., 2017) was also in the same ROS/MAP module as LAPTM5, ITGAM/CD11b and LILRB4.”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
ITGAM/CD11b was highlighted in recent genetic and functional analyses as likely being important for the progression of Alzheimer's disease, with expression driven by SPI1/PU.1 and related to amyloid deposition in mice and humans.
“ITGAM/CD11b was highlighted in recent genetic and functional analyses as likely being important for the progression of Alzheimer’s disease, whose expression was driven by SPI1/PU.1, and related to amyloid deposition in mice and humans (Zhang et al., 2013; Hong et al., 2016; Kamphuis et al., 2016; Olmos-Alonso et al., 2016; Huang et al., 2017; Nam et al., 2018).”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
Several of the predicted and established Alzheimer's disease risk genes may be regulated by SPI1/PU.1.
“Together, these findings suggest that several of the predicted and established Alzheimer’s disease risk genes may be regulated by SPI1/PU.1”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
This work is complementary to Huang et al. (2017), showing that microglial SPI1/PU.1-driven transcription is a common feature of many Alzheimer's disease loci.
“this work is complementary to that of Huang et al. (2017), showing that microglial SPI1/PU.1-driven transcription is a common feature of many Alzheimer’s disease loci.”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Huang et al., 2017
PU.1 has a role in antigen presentation and pro-inflammatory response.
“suggesting its role in antigen presentation and pro-inflammatory response by PU.1 [30, 31].”
From Transcriptional and Epigenetic Regulation of Microglia in Health and Disease (2019) · cites this paper as [31]
Silencing PU.1 in human microglia culture suppressed expression of DAP12/TYROBP, TREM2, LST1, CD11b, MRC1, CEBPA, PTPRC, and MHC class II genes HLA-DR, HLA-DP, and HLA-DQ.
“Silencing of PU.1 in human microglia culture suppressed expression of myeloid adapter protein DAP12/TYROBP, TREM2, LST1, CD11b, MRC1, CEBPA, PTPRC and MHC class II genes HLA-DR, HLA-DP and HLA-DQ [30, 31], suggesting its role in antigen presentation and pro-inflammatory response by PU.1 [30, 31].”
From Transcriptional and Epigenetic Regulation of Microglia in Health and Disease (2019) · cites this paper as [31]
For a number of genes including APOE, ABCA7, BIN1, TREM2, SORL1, ADAM10, and SPI1, there exists a number of functional studies and genetic evidence to suggest they are indeed the causal genes in their respective loci.
“For a number of genes including APOE, ABCA7, BIN1, TREM2, SORL1, ADAM10, and SPI1, there exists a number of functional studies and genetic evidence to suggest they are indeed the causal genes in their respective loci [97, 173], although their identification does not preclude the existence of additional pathogenic genes in these loci.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [173]
Huang et al. identified SPI1 as the most likely candidate causal gene in the genomic region traditionally annotated as the CELF1 locus.
“Huang et al. [173] leveraged this information to identify SPI1 as the most likely candidate causal gene in the genomic region traditionally annotated as the CELF1 locus due to the proximity of the lead GWAS SNP to the gene CELF1.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [173]
Regions of open chromatin in human monocytes, macrophages, and microglia containing AD risk variants were shown to be enriched for binding motifs of the transcription factor SPI1.
“In support of this hypothesis, regions of open chromatin in human monocytes, macrophages, and microglia [173, 184] containing AD risk variants were shown to be enriched for binding motifs of the transcription factor SPI1, providing a mechanistic explanation by which alterations in SPI1 levels conferred by variants in the SPI1 locus may have widespread effects on cellular function.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [173]
Huang et al. found a significant enrichment of AD SNPs in functional annotations attributed to hematopoietic cells, specifically monocytes and B-cells.
“Using a similar technique and annotation dataset, Huang et al. found a significant enrichment of AD SNPs in functional annotations attributed to hematopoietic cells, specifically monocytes and B-cells [173].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [173]
Common genetic variants impacting AD are not randomly distributed across the genome; instead, they are enriched for localization to regulatory elements active largely in human monocytes, macrophages, and microglia.
“we now understand that common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants seem to be enriched for localization to regulatory elements active largely in human monocytes, macrophages, and microglia.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [173]
Since the discovery that genetic variants are enriched within regulatory regions active in myeloid cells, a large emphasis has been placed on improving resources to better model the impact of these variants in a disease-relevant context.
“Since the discovery that genetic variants are enriched within regulatory regions active in myeloid cells [173, 183, 184], a large emphasis in the field has been placed on improving resources to better model the impact of these variants in a disease-relevant context.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [173]
Our findings for human AD are consistent with prior studies using independent datasets and modes of analysis which similarly implicated microglial and myeloid expression modules in AD risk and pathobiology.
“Our findings for human AD are consistent with prior studies using independent datasets and modes of analysis which similarly implicated microglial and myeloid expression modules in AD risk and pathobiology (89, 90).”
From The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease (2019) · cites this paper as [89]
MS4A6A showed a region-specific effect of age.
“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 [68]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Two P values are given for the rs1057233 CSF Aβ42 association
MinorThe results report P=8.24×10−4, but the discussion reports P=4.11×10−4 for the same SNP-biomarker association. The text does not distinguish these as different analyses.
“rs1057233 in the SPI1/CELF1 locus was significantly associated with CSF Aβ42 (P=8.24×10−4).”
“significantly associated with higher CSF Aβ42 (P=4.11×10−4)”
Two P values are given for the rs1057233 survival association
MinorThe survival results give P=5.4×10−6 for rs1057233, while the discussion gives P=8.4×10−6 for its association with later onset.
“rs1057233, located in the 3′UTR of a neighboring gene, SPI1, showed the strongest association (Table 1, P=5.4×10−6).”
“later AAO (P=8.4×10−6)”
CHS exclusion categories fall short of the reported total
MinorThe listed reasons account for 1,616 people, not the reported 1,908. The stated rule of listing only one reason per person does not explain the 292-person gap.
Reported 1908 · recomputed 1616
“The 1,908 persons excluded for prevalent CVD had prevalent coronary heart disease (n=1,195), congestive heart failure (n=86), peripheral vascular disease (n=93), valvular heart disease (n=20), stroke (n=166) or transient ischemic attack (n=56). Some persons had more than one reason to be excluded and for these individuals only the initial exclusionary event is listed.”
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793 · see citing works
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297 · see citing works
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities
Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,628 · see citing works
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 · see citing works
Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., Phatnani, H. P., Guarnieri, P., Caneda, C., Ruderisch, N., et al. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.1860-14.2014 · Cited by 4,875 · see citing works
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · see citing works
Self-citationGiambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261 · see citing works
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928 · see citing works
Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352 · see citing works
Harold, D., Abraham, R., Hollingworth, P., Sims, R., Gerrish, A., Hamshere, M. L., Pahwa, J. S., Moskvina, V., Dowzell, K., Williams, A., et al. (2009) · Nature Genetics · doi:10.1038/ng.440 · Cited by 3,326 · see citing works
Self-citation<i>TREM2</i> Variants in Alzheimer's Disease
Guerreiro, R., Wojtas, A., Bras, J., Carrasquillo, M., Rogaeva, E., Majounie, E., Cruchaga, C., Sassi, C., Kauwe, J. S. K., Younkin, S., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211851 · Cited by 3,020 · see citing works
Self-citation(2013) · The Lancet · doi:10.1016/s0140-6736(12)62129-1 · Cited by 3,010 · see citing works
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
MAGIC, Speliotes, E. K., on behalf of Procardis Consortium, Willer, C. J., Berndt, S. I., Monda, K. L., Thorleifsson, G., Jackson, A. U., Allen, H. L., Lindgren, C. M., et al. (2010) · Nature Genetics · doi:10.1038/ng.686 · Cited by 2,912 · see citing works
Self-citationLocusZoom: regional visualization of genome-wide association scan results
Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., & Willer, C. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq419 · Cited by 2,707 · see citing works
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604 · see citing works
Variant of <i>TREM2</i> Associated with the Risk of Alzheimer's Disease
Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., Bjornsson, S., Huttenlocher, J., Levey, A. I., Lah, J. J., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211103 · Cited by 2,558 · see citing works
Self-citationIdentification of a unique TGF-β–dependent molecular and functional signature in microglia
Butovsky, O., Jedrychowski, M. P., Moore, C. S., Cialic, R., Lanser, A. J., Gabriely, G., Koeglsperger, T., Dake, B., Wu, P. M., Doykan, C. E., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3599 · Cited by 2,543 · see citing works
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479 · see citing works
Zhang, Y., Sloan, S. A., Clarke, L. E., Caneda, C., Plaza, C. A., Blumenthal, P. D., Vogel, H., Steinberg, G. K., Edwards, M. S. B., Li, G., et al. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.013 · Cited by 2,295 · see citing works
Ward, L. D., & Kellis, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr917 · Cited by 2,229 · see citing works
Initiative, T. A. D. N., Hollingworth, P., CHARGE consortium, EADI1 consortium, Harold, D., Sims, R., Gerrish, A., Lambert, J.-C., Carrasquillo, M. M., Abraham, R., et al. (2011) · Nature Genetics · doi:10.1038/ng.803 · Cited by 1,997 · see citing works
Self-citationNaj, A. C., Jun, G., Beecham, G. W., Wang, L.-S., Vardarajan, B. N., Buros, J., Gallins, P. J., Buxbaum, J. D., Jarvik, G. P., Crane, P. K., et al. (2011) · Nature Genetics · doi:10.1038/ng.801 · Cited by 1,901 · see citing works
Self-citationCerebrospinal fluid and plasma biomarkers in Alzheimer disease
Blennow, K., Hampel, H., Weiner, M., & Zetterberg, H. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2010.4 · Cited by 1,863 · see citing works
New tools for studying microglia in the mouse and human CNS
Bennett, M. L., Bennett, F. C., Liddelow, S. A., Ajami, B., Zamanian, J. L., Fernhoff, N. B., Mulinyawe, S. B., Bohlen, C. J., Adil, A., Tucker, A., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1525528113 · Cited by 1,804 · see citing works
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657 · see citing works
Self-citationThe Rotterdam Study: objectives and design update
Albert Hofman, D., Breteler, M. M. B., van Duijn, C. M., Krestin, G. P., Pols, H. A. P., Stricker, B. H. C., Tiemeier, H. W., Uitterlinden, A. G., Vingerling, J. R., & Witteman, J. C. M. (2007) · European Journal of Epidemiology · doi:10.1007/s10654-007-9199-x · Cited by 1,404 · see citing works
Fagan, A. M., Mintun, M. A., Mach, R. H., Lee, S., Dence, C. S., Shah, A. R., LaRossa, G. N., Spinner, M. L., Klunk, W. E., Mathis, C. A., et al. (2006) · Annals of Neurology · doi:10.1002/ana.20730 · Cited by 1,246 · see citing works
Genome-wide Analysis of Genetic Loci Associated With Alzheimer Disease
Seshadri, S. (2010) · JAMA · doi:10.1001/jama.2010.574 · Cited by 1,219 · see citing works
Self-citationTargeted disruption of the PU.1 gene results in multiple hematopoietic abnormalities
McKercher, S. R., Torbett, B. E., Anderson, K. L., Henkel, G. W., Vestal, D. J., Baribault, H., Klemsz, M., Feeney, A. J., Wu, G. E., Paige, C. J., et al. (1996) · The EMBO Journal · doi:10.1002/j.1460-2075.1996.tb00949.x · Cited by 1,130 · see citing works
Wang, Y., Szretter, K. J., Vermi, W., Gilfillan, S., Rossini, C., Cella, M., Barrow, A. D., Diamond, M. S., & Colonna, M. (2012) · Nature Immunology · doi:10.1038/ni.2360 · Cited by 1,011 · see citing works
The framingham offspring study. Design and preliminary data
Feinleib, M., Kannel, W. B., Garrison, R. J., McNamara, P. M., & Castelli, W. P. (1975) · Preventive Medicine · doi:10.1016/0091-7435(75)90037-7 · Cited by 931 · see citing works
Splansky, G. L., Corey, D., Yang, Q., Atwood, L. D., Cupples, L. A., Benjamin, E. J., D'Agostino, R. B., Fox, C. S., Larson, M. G., Murabito, J. M., et al. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm021 · Cited by 860 · see citing works
A PCR primer bank for quantitative gene expression analysis
Wang, X. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng154 · Cited by 849 · see citing works
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
Speliotes, E. K., Willer, C. J., Berndt, S. I., Monda, K. L., Þorleifsson, G., Jackson, A., Allen, H. L., Lindgren, C. M., Luan, J., Mägi, R., et al. (2010) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/d3bn-7g86 · Cited by 767 · see citing works
Self-citationInnate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression
Fairfax, B. P., Humburg, P., Makino, S., Naranbhai, V., Wong, D., Lau, E., Jostins, L., Plant, K., Andrews, R., McGee, C., et al. (2014) · Science · doi:10.1126/science.1246949 · Cited by 764 · see citing works
Self-citationBeers, D. R., Henkel, J. S., Xiao, Q., Zhao, W., Wang, J., Yen, A. A., Siklos, L., McKercher, S. R., & Appel, S. H. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0607423103 · Cited by 733 · see citing works
DeCarli, C., Massaro, J., Harvey, D., Hald, J., Tullberg, M., Au, R., Beiser, A., D’Agostino, R., & Wolf, P. A. (2005) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2004.05.004 · Cited by 680 · see citing works
(2003) · Neuroepidemiology · doi:10.1159/000072920 · Cited by 672 · see citing works
Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease
Gjoneska, E., Pfenning, A. R., Mathys, H., Quon, G., Kundaje, A., Tsai, L.-H., & Kellis, M. (2015) · Nature · doi:10.1038/nature14252 · Cited by 576 · see citing works
CD33 Alzheimer's disease locus: altered monocyte function and amyloid biology
Initiative, T. A. D. N., Bradshaw, E. M., Chibnik, L. B., Keenan, B. T., Ottoboni, L., Raj, T., Tang, A., Rosenkrantz, L. L., Imboywa, S., Lee, M., et al. (2013) · Nature Neuroscience · doi:10.1038/nn.3435 · Cited by 570 · see citing works
Self-citationENCODE data at the ENCODE portal
Sloan, C. A., Chan, E. T., Davidson, J. M., Malladi, V. S., Strattan, J. S., Hitz, B. C., Gabdank, I., Narayanan, A. K., Ho, M., Lee, B. T., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1160 · Cited by 549 · see citing works
Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes
Raj, T., Rothamel, K., Mostafavi, S., Ye, C., Lee, M. N., Replogle, J. M., Feng, T., Lee, M., Asinovski, N., Frohlich, I., et al. (2014) · Science · doi:10.1126/science.1249547 · Cited by 506 · see citing works
Self-citationDagher, N. N., Najafi, A. R., Kayala, K. M. N., Elmore, M. R. P., White, T. E., Medeiros, R., West, B. L., & Green, K. N. (2015) · Journal of Neuroinflammation · doi:10.1186/s12974-015-0366-9 · Cited by 505 · see citing works
Incidence of dementia and probable Alzheimer's disease in a general population: The Framingham Study
Bachman, D. L., Wolf, P. A., Linn, R. T., Knoefel, J. E., Cobb, J. L., Belanger, A. J., White, L. R., & D'Agostino, R. B. (1993) · Neurology · doi:10.1212/wnl.43.3_part_1.515 · Cited by 470 · see citing works
Retrospective Studies and Failure Time Models
Prentice, R. L., & Breslow, N. E. (1978) · Biometrika · doi:10.1093/biomet/65.1.153 · Cited by 427 · see citing works
Slooter, A. J. C., Cruts, M., Kalmijn, S., Hofman, A., Breteler, M. M. B., Van Broeckhoven, C., & van Duijn, C. M. (1998) · Archives of Neurology · doi:10.1001/archneur.55.7.964 · Cited by 399 · see citing works
GWAS of Cerebrospinal Fluid Tau Levels Identifies Risk Variants for Alzheimer’s Disease
Cruchaga, C., Kauwe, J. S. K., Harari, O., Jin, S. C., Cai, Y., Karch, C. M., Benitez, B. A., Jeng, A. T., Skorupa, T., Carrell, D., et al. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.026 · Cited by 383 · see citing works
Self-citationLifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study
Seshadri, S. S., & Wolf, P. A. (2007) · The Lancet Neurology · doi:10.1016/s1474-4422(07)70291-0 · Cited by 368 · see citing works
Self-citationStrawbridge, R. J., Dupuis, J., Prokopenko, I., Barker, A., Ahlqvist, E., Rybin, D., Petrie, J. R., Travers, M. E., Bouatia-Naji, N., Dimas, A. S., et al. (2011) · Diabetes · doi:10.2337/db11-0415 · Cited by 356 · see citing works
Roe, J., Mercan, F., Rivera, K. D., Pappin, D., & Vakoc, C. R. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.011 · Cited by 332 · see citing works
The Rotterdam Study: 2014 objectives and design update
Albert Hofman, D., Murad, S. D., van Duijn, C. M., Franco, O. H., Goedegebure, A., Ikram, M. A., Klaver, C. C. W., Nijsten, T. E. C., Peeters, R. P., Stricker, B. H. C., et al. (2013) · European Journal of Epidemiology · doi:10.1007/s10654-013-9866-z · Cited by 330 · see citing works
Self-citationLoss-of-function variants in ABCA7 confer risk of Alzheimer's disease
DemGene, Steinberg, S., Stefansson, H., Jonsson, T., Johannsdottir, H., Ingason, A., Helgason, H., Sulem, P., Magnusson, O. T., Gudjonsson, S. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3246 · Cited by 324 · see citing works
A Polymorphism in CALHM1 Influences Ca2+ Homeostasis, Aβ Levels, and Alzheimer's Disease Risk
Dreses‐Werringloer, U., Lambert, J., Vingtdeux, V., Zhao, H., Vais, H., Siebert, A. P., Jain, A., Koppel, J. L., Rovelet‐Lecrux, A., Hannequin, D., et al. (2008) · Cell · doi:10.1016/j.cell.2008.05.048 · Cited by 319 · see citing works
Self-citationGrimmer, T., Riemenschneider, M., Förstl, H., Henriksen, G., Klunk, W. E., Mathis, C. A., Shiga, T., Wester, H.-J., Kurz, A., & Drzezga, A. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.01.027 · Cited by 278 · see citing works
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works
Evaluation of Dementia in the Cardiovascular Health Cognition Study
López, O. L., Kuller, L. H., Fitzpatrick, A. L., Ives, D. G., Becker, J. T., & Beauchamp, N. (2003) · Neuroepidemiology · doi:10.1159/000067110 · Cited by 238 · see citing works
Self-citationExploring long-range genome interactions using the WashU Epigenome Browser
Zhou, X., Lowdon, R. F., Li, D., Lawson, H. A., Madden, P. A. F., Costello, J., & Wang, T. (2013) · Nature Methods · doi:10.1038/nmeth.2440 · Cited by 209 · see citing works
Naj, A. C., Jun, G., Reitz, C., Kunkle, B. W., Perry, W., Park, Y. S., Beecham, G. W., Rajbhandary, R. A., Hamilton-Nelson, K. L., Wang, L.-S., et al. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2014.1491 · Cited by 185 · see citing works
Self-citationThe Framingham Study An Epidemiological Approach to Coronary Heart Disease
Dawber, T. R., & Kannel, W. B. (1966) · Circulation · doi:10.1161/01.cir.34.4.553 · Cited by 177 · see citing works
CD33 modulates TREM2: convergence of Alzheimer loci
Chan, G., White, C. C., Winn, P. A., Cimpean, M., Replogle, J. M., Glick, L. R., Cuerdon, N. E., Ryan, K. J., Johnson, K. A., Schneider, J. A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4126 · Cited by 162 · see citing works
Pham, T.-H., Benner, C., Lichtinger, M., Schwarzfischer, L., Hu, Y., Andreesen, R., Chen, W., & Rehli, M. (2012) · Blood · doi:10.1182/blood-2012-01-402453 · Cited by 143 · see citing works
The transcription factor PU.1 is critical for viability and function of human brain microglia
Smith, A. M., Gibbons, H. M., Oldfield, R. L., Bergin, P. M., Mee, E. W., Faull, R. L. M., & Dragunow, M. (2013) · Glia · doi:10.1002/glia.22486 · Cited by 141 · see citing works
Self-citationNew Norms for a New Generation: Cognitive Performance in the Framingham Offspring Cohort
Au, R., Seshadri, S. S., Wolf, P. A., Elias, M. F., Elias, P. K., SULLIVAN, L. M., Beiser, A., & D’Agostino, R. B. (2004) · Experimental Aging Research · doi:10.1080/03610730490484380 · Cited by 137 · see citing works
Self-citationPeskind, E. R., Li, G., Shofer, J., Quinn, J. F., Kaye, J. A., Clark, C. M., Farlow, M. R., DeCarli, C., Raskind, M. A., Schellenberg, G. D., et al. (2006) · Archives of Neurology · doi:10.1001/archneur.63.7.936 · Cited by 131 · see citing works
Self-citationGriffon, A., Barbier, Q., Dalino, J., van Helden, J., Spicuglia, S., & Ballester, B. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1280 · Cited by 123 · see citing works
Genome-wide association analysis of age-at-onset in Alzheimer's disease
Kamboh, M. I., Barmada, M. M., Demirci, F. Y., Minster, R. L., Carrasquillo, M. M., Pankratz, V. S., Younkin, S. G., Saykin, A. J., Initiative, T. A. D. N., Sweet, R. A., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.135 · Cited by 100 · see citing works
Self-citationThe active enhancer network operated by liganded RXR supports angiogenic activity in macrophages
Daniel, B., Nagy, G., Hah, N., Horvath, A., Czimmerer, Z., Poliska, S., Gyuris, T., Keirsse, J., Gysemans, C., Van Ginderachter, J. A., et al. (2014) · Genes & Development · doi:10.1101/gad.242685.114 · Cited by 97 · see citing works
Alzheimer’s disease risk variants show association with cerebrospinal fluid amyloid beta
Kauwe, J. S. K., Wang, J., Mayo, K., Morris, J. C., Fagan, A. M., Holtzman, D. M., & Goate, A. M. (2009) · neurogenetics · doi:10.1007/s10048-008-0150-4 · Cited by 82 · see citing works
Self-citationPU.1 and Haematopoietic Cell Fate: Dosage Matters
Mak, K. S., Funnell, A. P. W., Pearson, R. C. M., & Crossley, M. (2011) · International Journal of Cell Biology · doi:10.1155/2011/808524 · Cited by 77 · see citing works
A comprehensive profile of ChIP-Seq-based PU.1/Spi1 target genes in microglia.
Satoh, J.-I., Asahina, N., Kitano, S., & Kino, Y. (2014) · Gene Regulation and Systems Biology · doi:10.4137/grsb.s19711 · Cited by 74 · see citing works
van der Net, J. B., Janssens, A. C. J. W., Eijkemans, M. J. C., Kastelein, J. J. P., Sijbrands, E. J. G., & Steyerberg, E. W. (2008) · European Journal of Human Genetics · doi:10.1038/ejhg.2008.59 · Cited by 74 · see citing works
NEUROPSYCHOLOGICAL TEST PERFORMANCE IN FRAMINGHAM: A DESCRIPTIVE STUDY
Farmer, M. E., White, L. R., Kittner, S. J., Kaplan, E., Moes, E., McNamara, P. S., WOLZ, M. M., Wolf, P. A., & Feinleib, M. (1987) · Psychological Reports · doi:10.2466/pr0.1987.60.3c.1023 · Cited by 73 · see citing works
Bennett, C., Crawford, F., Osborne, A., Diaz, P., Hoyne, J., Lopez, R., Roques, P., Duara, R., Rossor, M., & Mullan, M. (1995) · American Journal of Medical Genetics · doi:10.1002/ajmg.1320600102 · Cited by 67 · see citing works
Safety of Lumbar Puncture Procedures in Patients with Alzheimers Disease
Peskind, E., Nordberg, A., Darreh-Shori, T., & Soininen, H. (2009) · Current Alzheimer Research · doi:10.2174/156720509788486509 · Cited by 66 · see citing works
Hikami, K., Kawasaki, A., Ito, I., Koga, M., Ito, S., Hayashi, T., Matsumoto, I., Tsutsumi, A., Kusaoi, M., Takasaki, Y., et al. (2011) · Arthritis & Rheumatism · doi:10.1002/art.30188 · Cited by 58 · see citing works
Genome-Wide Haplotype Analysis of Cis Expression Quantitative Trait Loci in Monocytes
Garnier, S., Trương, V., Brocheton, J., Zeller, T., Rovital, M., Wild, P. S., Ziegler, A., Münzel, T. F., Tiret, L., Blankenberg, S., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003240 · Cited by 56 · see citing works
Impaired glucose tolerance in midlife and longitudinal changes in brain function during aging
Thambisetty, M., Beason-Held, L. L., An, Y., Kraut, M., Metter, J., Egan, J., Ferrucci, L., O'Brien, R., & Resnick, S. M. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.03.025 · Cited by 54 · see citing works
Evidence that PICALM affects age at onset of Alzheimer's dementia in Down syndrome
Jones, E. L., Mok, K., Hanney, M., Harold, D., Sims, R., Williams, J., & Ballard, C. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.03.018 · Cited by 47 · see citing works
Self-citationA model for modulation of leptin activity by association with clusterin
Bajari, T. M., Strasser, V., Nimpf, J., & Schneider, W. J. (2003) · The FASEB Journal · doi:10.1096/fj.02-1106fje · Cited by 45 · see citing works
氷上, 光. (2011) · Terrestrial Environment Research Center (University of Tsukuba) · Cited by 42 · see citing works
Alzheimer's disease risk genes and the age-at-onset phenotype
Thambisetty, M., An, Y., & Tanaka, T. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.05.028 · Cited by 40 · see citing works
Sakai, K., Tiebel, O., Ljungberg, M. C., Sullivan, M., Lee, H.-J., Terashima, T., Li, R., Kobayashi, K., Lu, H.-C., Chan, L., et al. (2009) · Brain Research · doi:10.1016/j.brainres.2009.04.030 · Cited by 23 · see citing works
- Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Self-citationBlood-Brain Barrier: From Physiology to Disease and Back
Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R., & Zlokovic, B. V. (2019) · Physiological Reviews · doi:10.1152/physrev.00050.2017 · Cited by 2,294 · see citing works
Microglia in Alzheimer’s disease
Hansen, D. V., Hanson, J. E., & Sheng, M. (2018) · Journal of Cell Biology · doi:10.1083/jcb.201709069 · Cited by 1,935 · see citing works
Finucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0081-4 · Cited by 1,097 · see citing works
Microglial signatures and their role in health and disease
Butovsky, O., & Weiner, H. L. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0057-5 · Cited by 1,046 · see citing works
Grubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R. K., Buckberry, S., Vargas-Landin, D. B., Poppe, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0539-4 · Cited by 956 · see citing works
Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease
Guo, T., Zhang, D., Zeng, Y., Huang, T. Y., Xu, H., & Zhao, Y. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00391-7 · Cited by 994 · see citing works
Spangenberg, E., Severson, P. L., Hohsfield, L. A., Crapser, J., Zhang, J., Burton, E. A., Zhang, Y., Spevak, W., Lin, J., Phan, N. Y., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11674-z · Cited by 939 · see citing works
Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association
Nott, A., Holtman, I. R., Coufal, N. G., Schlachetzki, J. C. M., Yu, M., Hu, R., Han, C. Z., Pena, M., Xiao, J., Wu, Y., et al. (2019) · Science · doi:10.1126/science.aay0793 · Cited by 823 · see citing works
Microglia in Neurological Diseases: A Road Map to Brain-Disease Dependent-Inflammatory Response
Bachiller, S., Jiménez-Ferrer, I., Paulus, A., Yang, Y., Swanberg, M., Deierborg, T., & Boza‐Serrano, A. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00488 · Cited by 706 · see citing works
Alzheimer’s disease: A matter of blood–brain barrier dysfunction?
Montagne, A., Zhao, Z., & Zlokovic, B. V. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20171406 · Cited by 672 · see citing works
Macrophages in health and disease
Park, M. D., Silvin, A., Ginhoux, F., & Merad, M. (2022) · Cell · doi:10.1016/j.cell.2022.10.007 · Cited by 644 · see citing works
Interplay between innate immunity and Alzheimer disease: APOE and TREM2 in the spotlight
Shi, Y., & Holtzman, D. M. (2018) · Nature Reviews Immunology · doi:10.1038/s41577-018-0051-1 · Cited by 595 · see citing works
Late onset Alzheimer’s disease genetics implicates microglial pathways in disease risk
Efthymiou, A. G., & Goate, A. M. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0184-x · Cited by 562 · see citing works
Self-citationA transcriptomic atlas of aged human microglia
Olah, M., Patrick, E., Villani, A.-C., Xu, J., White, C. C., Ryan, K. J., Piehowski, P., Kapasi, A., Nejad, P., Cimpean, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-02926-5 · Cited by 482 · see citing works
The multiplex model of the genetics of Alzheimer's disease
Sims, R., Hill, M., & Williams, J. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0599-5 · Cited by 466 · see citing works
Self-citationA statistical framework for cross-tissue transcriptome-wide association analysis
Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., Yu, Z., Li, B., Gu, J., Muchnik, S. K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0345-7 · Cited by 401 · see citing works
Modeling Alzheimer’s disease with iPSC-derived brain cells
Penney, J., Ralvenius, W. T., & Tsai, L.-H. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0468-3 · Cited by 471 · see citing works
CD22 blockade restores homeostatic microglial phagocytosis in ageing brains
Pluvinage, J. V., Haney, M. S., Smith, B. A. H., Sun, J., Iram, T., Bonanno, L., Li, L., Lee, D. P., Morgens, D. W., Yang, A. C., et al. (2019) · Nature · doi:10.1038/s41586-019-1088-4 · Cited by 459 · see citing works
Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation
Srinivasan, K., Friedman, B. A., Etxeberria, A., Huntley, M. A., van der Brug, M. P., Foreman, O., Paw, J. S., Modrusan, Z., Beach, T. G., Serrano, G. E., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107843 · Cited by 427 · see citing works
Meningeal 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 · see citing works
Self-citationLee, C. Y. D., Daggett, A., Gu, X., Jiang, L.-L., Langfelder, P., Li, X., Wang, N., Zhao, Y., Park, C. S., Cooper, Y., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.02.002 · Cited by 396 · see citing works
Immune Signaling in Neurodegeneration
Hammond, T. R., Marsh, S. E., & Stevens, B. (2019) · Immunity · doi:10.1016/j.immuni.2019.03.016 · Cited by 336 · see citing works
Phagocytosis in the Brain: Homeostasis and Disease
Galloway, D. A., Phillips, A. E. M., Owen, D. R., & Moore, C. S. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00790 · Cited by 304 · see citing works
Yang, C., Farias, F. H. G., Ibanez, L., Suhy, A., Sadler, B., Fernandez, M. V., Wang, F., Bradley, J. L., Eiffert, B., Bahena, J. A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00886-6 · Cited by 290 · see citing works
Self-citationThe identity and function of microglia in neurodegeneration
Song, W. M., & Colonna, M. (2018) · Nature Immunology · doi:10.1038/s41590-018-0212-1 · Cited by 308 · see citing works
The Therapeutic Potential of Metformin in Neurodegenerative Diseases
Rotermund, C., Machetanz, G., & Fitzgerald, J. C. (2018) · Frontiers in Endocrinology · doi:10.3389/fendo.2018.00400 · Cited by 310 · see citing works
The neuroimmune axis of Alzheimer’s disease
Jorfi, M., Maaser‐Hecker, A., & Tanzi, R. E. (2023) · Genome Medicine · doi:10.1186/s13073-023-01155-w · Cited by 299 · see citing works
The good, the bad, and the opportunities of the complement system in neurodegenerative disease
Schartz, N. D., & Tenner, A. J. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-02024-8 · Cited by 283 · see citing works
Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology
Sierksma, A., Lu, A., Mancuso, R., Fattorelli, N., Thrupp, N., Salta, E., Zoco, J., Blum, D., Buée, L., De Strooper, B., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201910606 · Cited by 291 · see citing works
Fernandez, C. G., Hamby, M. E., McReynolds, M. L., & Ray, W. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00014 · Cited by 277 · see citing works
Lopes, K. D. P., Snijders, G. J. L., Humphrey, J., Allan, A., Sneeboer, M. A. M., Navarro, E., Schilder, B. M., Vialle, R. A., Parks, M., Missall, R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00976-y · Cited by 273 · see citing works
Self-citationThe <i>MS4A</i> gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk
Deming, Y., Filipello, F., Cignarella, F., Cantoni, C., Hsu, S., Mikesell, R., Li, Z., Del-Aguila, J. L., Dube, U., Farias, F. G., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aau2291 · Cited by 271 · see citing works
Self-citationRisk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
Zhang, Q., Sidorenko, J., Couvy-Duchesne, B., Marioni, R. E., Wright, M. J., Goate, A. M., Marcora, E., Huang, K.-L., Porter, T., Laws, S. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18534-1 · Cited by 272 · see citing works
Self-citationZhong, L., Wang, Z., Wang, D., Wang, Z., Martens, Y. A., Wu, L., Xu, Y., Wang, K., Li, J., Huang, R., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0247-7 · Cited by 217 · see citing works
Alzheimer’s Disease: From Firing Instability to Homeostasis Network Collapse
Frère, S. G. A., & Slutsky, I. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.028 · Cited by 230 · see citing works
Roy, E. R., Chiu, G., Li, S., Propson, N. E., Kanchi, R., Wang, B., Coarfa, C., Zheng, H., & Cao, W. (2022) · Immunity · doi:10.1016/j.immuni.2022.03.018 · Cited by 236 · see citing works
Novikova, G., Kapoor, M., TCW, J., Abud, E. M., Efthymiou, A. G., Chen, S. X., Cheng, H., Fullard, J. F., Bendl, J., Liu, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21823-y · Cited by 214 · see citing works
Self-citationMicroglia in Brain Development, Homeostasis, and Neurodegeneration
Bohlen, C. J., Friedman, B. A., Dejanovic, B., & Sheng, M. (2019) · Annual Review of Genetics · doi:10.1146/annurev-genet-112618-043515 · Cited by 212 · see citing works
Untangling Genetic Risk for Alzheimer’s Disease
Pimenova, A. A., Raj, T., & Goate, A. M. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.05.014 · Cited by 223 · see citing works
Self-citationEmerging Microglia Biology Defines Novel Therapeutic Approaches for Alzheimer’s Disease
Lewcock, J. W., Schlepckow, K., Di Paolo, G., Tahirovic, S., Monroe, K. M., & Haass, C. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.029 · Cited by 221 · see citing works
Genetics of Alzheimer’s disease: where we are, and where we are going
Bellenguez, C., Grenier-Boley, B., & Lambert, J.-C. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.11.024 · Cited by 215 · see citing works
Self-citationEndo-lysosomal dysregulations and late-onset Alzheimer’s disease: impact of genetic risk factors
Van Acker, Z. P., Bretou, M., & Annaert, W. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0323-7 · Cited by 222 · see citing works
Microglial Phagocytosis: A Disease-Associated Process Emerging from Alzheimer’s Disease Genetics
Podleśny-Drabiniok, A., Marcora, E., & Goate, A. M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.10.002 · Cited by 211 · see citing works
Self-citationLiu, W., Taso, O., Wang, R., Bayram, S., Graham, A. C., Garcia-Reitboeck, P., Mallach, A., Andrews, W. D., Piers, T. M., Botia, J. A., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa209 · Cited by 214 · see citing works
Zhang, Y., Lu, Q., Ye, Y., Huang, K., Liu, W., Wu, Y., Zhong, X., Li, B., Yu, Z., Travers, B. G., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02478-w · Cited by 198 · see citing works
Self-citationAPOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints
Yin, Z., Rosenzweig, N., Kleemann, K. L., Zhang, X., Brandão, W., Margeta, M. A., Schroeder, C., Sivanathan, K. N., Silveira, S., Gauthier, C., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01627-6 · Cited by 202 · see citing works
Understanding Alzheimer Disease at the Interface between Genetics and Transcriptomics
Verheijen, J., & Sleegers, K. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.02.007 · Cited by 189 · see citing works
Polygenic risk score analysis of pathologically confirmed Alzheimer disease
Escott‐Price, V., Myers, A., Huentelman, M. J., & Hardy, J. (2017) · Annals of Neurology · doi:10.1002/ana.24999 · Cited by 178 · see citing works
Self-citationHorgusluoglu, E., Neff, R., Song, W., Wang, M., Wang, Q., Arnold, M., Krumsiek, J., Galindo‐Prieto, B., Ming, C., Nho, K., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12468 · Cited by 175 · see citing works
Self-citationPU.1 regulates Alzheimer’s disease-associated genes in primary human microglia
Rustenhoven, J., Smith, A. M., Smyth, L. C., Jansson, D., Scotter, E. L., Swanson, M. E. V., Aalderink, M., Coppieters, N., Narayan, P., Handley, R., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0277-1 · Cited by 155 · see citing works
Self-citationNeuroimmune Connections in Aging and Neurodegenerative Diseases
Scheiblich, H., Trombly, M., Ramirez, A., & Heneka, M. T. (2020) · Trends in Immunology · doi:10.1016/j.it.2020.02.002 · Cited by 166 · see citing works
Genetics of the human microglia regulome refines Alzheimer’s disease risk loci
Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01149-1 · Cited by 168 · see citing works
Self-citationDourlen, P., Kilinc, D., Malmanche, N., Chapuis, J., & Lambert, J.-C. (2019) · Acta Neuropathologica · doi:10.1007/s00401-019-02004-0 · Cited by 168 · see citing works
Self-citationCRISPR-based functional genomics for neurological disease
Kampmann, M. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0373-z · Cited by 163 · see citing works
Insights into Alzheimer’s disease from single-cell genomic approaches
Murdock, M. H., & Tsai, L.-H. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01222-2 · Cited by 148 · see citing works
Zhang, L., Silva, T. C., Young, J. I., Gomez, L., Schmidt, M. A., Hamilton-Nelson, K. L., Kunkle, B. W., Chen, X., Martin, E. R., & Wang, L. (2020) · Nature Communications · doi:10.1038/s41467-020-19791-w · Cited by 139 · see citing works
Causal associations between modifiable risk factors and the Alzheimer's phenome
Andrews, S. J., Fulton‐Howard, B., O'Reilly, P., Marcora, E., Goate, A. M., & collaborators of the Alzheimer's Disease Genetics Consortium (2021) · Annals of Neurology · doi:10.1002/ana.25918 · Cited by 156 · see citing works
Self-citationGirdhar, K., Hoffman, G. E., Jiang, Y., Brown, L., Kundakovic, M., Hauberg, M. E., Francoeur, N. J., Wang, Y.-C., Shah, H., Kavanagh, D. H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0187-0 · Cited by 139 · see citing works
Selene: a PyTorch-based deep learning library for sequence data
Chen, K., Cofer, E. M., Zhou, J., & Troyanskaya, O. G. (2019) · Nature Methods · doi:10.1038/s41592-019-0360-8 · Cited by 132 · see citing works
Translating genetic risk of Alzheimer’s disease into mechanistic insight and drug targets
Sierksma, A. S. R., Escott‐Price, V., & Strooper, B. D. (2020) · Science · doi:10.1126/science.abb8575 · Cited by 149 · see citing works
Self-citationTranscriptional and Epigenetic Regulation of Microglia in Health and Disease
Yeh, H., & Ikezu, T. (2019) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2018.11.004 · Cited by 135 · see citing works
Genetic variants and functional pathways associated with resilience to Alzheimer’s disease
Dumitrescu, L. C., Mahoney, E. R., Mukherjee, S., Lee, M. L., Bush, W. S., Engelman, C. D., Lu, Q., Fardo, D. W., Trittschuh, E. H., Mez, J. B., et al. (2020) · Brain · doi:10.1093/brain/awaa209 · Cited by 140 · see citing works
Self-citationHuman iPSC‐derived microglia: A growing toolset to study the brain's innate immune cells
Hasselmann, J., & Blurton‐Jones, M. (2020) · Glia · doi:10.1002/glia.23781 · Cited by 145 · see citing works
Functional and structural damage of neurons by innate immune mechanisms during neurodegeneration
Ising, C., & Heneka, M. T. (2018) · Cell Death and Disease · doi:10.1038/s41419-017-0153-x · Cited by 136 · see citing works
Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response
Pimenova, A. A., Herbinet, M., Gupta, I., Machlovi, S. I., Bowles, K. R., Marcora, E., & Goate, A. M. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.105217 · Cited by 141 · see citing works
Self-citationDissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease
Broce, I. J., Tan, C. H., Fan, C. C., Jansen, I., Savage, J. E., Witoelar, A., Wen, N., Hess, C. P., Dillon, W. P., Glastonbury, C. M., et al. (2019) · Acta Neuropathologica · doi:10.1007/s00401-018-1928-6 · Cited by 140 · see citing works
Self-citationFinucane, H. K., Reshef, Y., Anttila, V., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/103069 · Cited by 136 · see citing works
Shireby, G. L., Dempster, E. L., Policicchio, S. S. S., Smith, R. G., Pishva, E., Chioza, B. A., Davies, J. P., Burrage, J., Lunnon, K., Vellame, D. S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33394-7 · Cited by 125 · see citing works
Developing Effective Alzheimer’s Disease Therapies: Clinical Experience and Future Directions
Elmaleh, D. R., Farlow, M. R., Conti, P. S., Tompkins, R. G., Kundakovic, L., & Tanzi, R. E. (2019) · Journal of Alzheimer’s Disease · doi:10.3233/jad-190507 · Cited by 129 · see citing works
Lau, S.-F., Chen, C., Fu, W.-Y., Qu, J. Y., Cheung, T. H., Fu, A. K. Y., & Ip, N. Y. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107530 · Cited by 114 · see citing works
Genetic architecture of Alzheimer's disease
Neuner, S. M., TCW, J., & Goate, A. M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104976 · Cited by 126 · see citing works
Self-citationInflammatory mechanisms in neurodegeneration
Nichols, M. R., St‐Pierre, M., Wendeln, A., Makoni, N. J., Gouwens, L. K., Garrad, E. C., Sohrabi, M., Neher, J. J., Tremblay, M., & Combs, C. K. (2019) · Journal of Neurochemistry · doi:10.1111/jnc.14674 · Cited by 122 · see citing works
Gao, S., Shi, Q., Zhang, Y., Liang, G., Kang, Z., Huang, B., Ma, D., Wang, L., Jiao, J., Fang, X., et al. (2021) · Cell Research · doi:10.1038/s41422-021-00540-7 · Cited by 112 · see citing works
Alzheimer’s Disorder: Epigenetic Connection and Associated Risk Factors
Sharma, V., Mehta, V., & Singh, T. G. (2020) · Current Neuropharmacology · doi:10.2174/1570159x18666200128125641 · Cited by 121 · see citing works
BIN1 is a key regulator of proinflammatory and neurodegeneration-related activation in microglia
Sudwarts, A., Ramesha, S., Gao, T., Ponnusamy, M., Wang, S., Hansen, M. T., Kozlova, A., Bitarafan, S., Kumar, P., Beaulieu‐Abdelahad, D., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00535-x · Cited by 107 · see citing works
Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk
Salih, D. A., Bayram, S., Guelfi, S., Reynolds, R. H., Shoai, M., Ryten, M., Brenton, J. W., Zhang, D., Matarin, M., Botia, J. A., et al. (2019) · Brain Communications · doi:10.1093/braincomms/fcz022 · Cited by 110 · see citing works
Self-citationLu, Q., Powles, R. L., Abdallah, S., Ou, D., Wang, Q., Hu, Y., Lu, Y., Liu, W., Li, B., Mukherjee, S., et al. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006933 · Cited by 97 · see citing works
Tansey, K. E., Cameron, D., & Hill, M. J. (2018) · Genome Medicine · doi:10.1186/s13073-018-0523-8 · Cited by 115 · see citing works
New tricks for an ancient system: Physiological and pathological roles of complement in the CNS
Tenner, A. J., Stevens, B., & Woodruff, T. M. (2018) · Molecular Immunology · doi:10.1016/j.molimm.2018.06.264 · Cited by 112 · see citing works
Hernandez, M. X., Jiang, S., Cole, T. A., Chu, S., Fonseca, M. I., Fang, M. J., Hohsfield, L. A., Torres, M. D., Green, K. N., Wetsel, R. A., et al. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0210-z · Cited by 101 · see citing works
Microglial efferocytosis: Diving into the Alzheimer’s disease gene pool
Romero-Molina, C., Garretti, F., Andrews, S. J., Marcora, E., & Goate, A. M. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.015 · Cited by 111 · see citing works
Self-citationSex-specific genetic predictors of Alzheimer’s disease biomarkers
(ADNI), A. D. N. I., Deming, Y., The Alzheimer Disease Genetics Consortium (ADGC), Dumitrescu, L., Barnes, L. L., Thambisetty, M., Kunkle, B., Gifford, K. A., Bush, W. S., Chibnik, L. B., et al. (2018) · Acta Neuropathologica · doi:10.1007/s00401-018-1881-4 · Cited by 110 · see citing works
Zhou, X., Chen, Y., Mok, K. Y., Zhao, Q., Chen, K., Chen, Y., Hardy, J., Li, Y., Fu, A. K. Y., Guo, Q., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715554115 · Cited by 96 · see citing works
Self-citationThe Genetics of Dementia with Lewy Bodies: Current Understanding and Future Directions
Orme, T., Guerreiro, R. J., & Brás, J. (2018) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-018-0874-y · Cited by 101 · see citing works
Genomics of Alzheimer’s disease implicates the innate and adaptive immune systems
Li, Y., Laws, S. M., Miles, L. A., Wiley, J. S., Huang, X., Masters, C. L., & Gu, B. J. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03986-5 · Cited by 93 · see citing works
Keaney, J., Gasser, J., Gillet, G., Scholz, D., & Kadiu, I. (2019) · Journal of Neuroimmune Pharmacology · doi:10.1007/s11481-019-09839-0 · Cited by 98 · see citing works
Treur, J. L., Munafo, M. R., Logtenberg, E., Wiers, R. W. H. J., & Verweij, K. J. H. (2021) · Psychological Medicine · doi:10.1017/s003329172100180x · Cited by 94 · see citing works
Sex differences in the genetic predictors of Alzheimer’s pathology
Dumitrescu, L., Barnes, L. L., Thambisetty, M., Beecham, G., Kunkle, B., Bush, W. S., Gifford, K. A., Chibnik, L. B., Mukherjee, S., De Jager, P. L., et al. (2019) · Brain · doi:10.1093/brain/awz206 · Cited by 93 · see citing works
The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease
Bonham, L. W., Sirkis, D. W., & Yokoyama, J. S. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01170 · Cited by 83 · see citing works
Association between alcohol consumption and Alzheimer's disease: A Mendelian randomization study
Andrews, S. J., Goate, A., & Anstey, K. J. (2020) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2019.09.086 · Cited by 82 · see citing works
Self-citationA neurodegenerative cellular stress response linked to dark microglia and toxic lipid secretion
Flury, A. G., Aljayousi, L., Park, H.-J., Khakpour, M., Mechler, J., Aziz, S., McGrath, J. D., Deme, P., Sandberg, C., Ibáñez, F. G., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.11.018 · Cited by 98 · see citing works
Biological insights from multi-omic analysis of 31 genomic risk loci for adult hearing difficulty
Kalra, G., Milon, B. C., Casella, A. M., Herb, B. R., Humphries, E., Song, Y., Rose, K. P., Hertzano, R. P., & Ament, S. A. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009025 · Cited by 77 · see citing works
APOE4 confers transcriptomic and functional alterations to primary mouse microglia
Machlovi, S. I., Neuner, S. M., Hemmer, B. M., Khan, R., Liu, Y., Huang, M., Zhu, J. D., Castellano, J. M., Cai, D., Marcora, E., et al. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105615 · Cited by 73 · see citing works
Self-citationPearl, J. R., Colantuoni, C., Bergey, D. E., Funk, C. C., Shannon, P. T., Basu, B., Casella, A. M., Oshone, R. T., Hood, L., Price, N. D., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.01.002 · Cited by 60 · see citing works
Shared genetic etiology underlying Alzheimer’s disease and major depressive disorder
Lutz, M. W., Sprague, D., Barrera, J., & Chiba‐Falek, O. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0769-y · Cited by 70 · see citing works
Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework
Ning, K., Chen, B., Sun, F., Hobel, Z. B., Zhao, L., Matloff, W. J., & Toga, A. W. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.04.009 · Cited by 76 · see citing works
Neuroimmune contributions to Alzheimer’s disease: a focus on human data
Haage, V., & De Jager, P. L. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01637-0 · Cited by 74 · see citing works
Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s disease
Morabito, S., Miyoshi, E., Michael, N., & Swarup, V. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa182 · Cited by 74 · see citing works
Initiative, A. D. N., Tan, M.-S., Yang, Y.-X., Xu, W., Wang, H.-F., Tan, L., Zuo, C.-T., Dong, Q., Tan, L., Suckling, J., et al. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00755-7 · Cited by 71 · see citing works
- Authors
- The International Genomics of Alzheimer's Project, Huang, K.-L., Initiative, T. A. D. N., Marcora, E., Pimenova, A. A., Di Narzo, A. F., Kapoor, M., Jin, S. C., Harari, O., Bertelsen, S., Fairfax, B. P., Czajkowski, J., Chouraki, V., Grenier-Boley, B., Bellenguez, C., Deming, Y., McKenzie, A., Raj, T., Renton, A. E., Budde, J., Smith, A., Fitzpatrick, A., Bis, J. C., DeStefano, A., Adams, H. H. H., Ikram, M. A., van der Lee, S., Del-Aguila, J. L., Fernandez, M. V., Ibañez, L., Sims, R., Escott-Price, V., Mayeux, R., Haines, J. L., Farrer, L. A., Pericak-Vance, M. A., Lambert, J. C., van Duijn, C., Launer, L., Seshadri, S., Williams, J., Amouyel, P., Schellenberg, G. D., Zhang, B., Borecki, I., Kauwe, J. S. K., Cruchaga, C., Hao, K., & Goate, A. M.
- Affiliations
- Washington University in St. Louis (US)
- Icahn School of Medicine at Mount Sinai (US)
- Yale University (US)
- Centre for Human Genetics (GB)
- Boston University (US)
- Inserm (FR)
- University of Iceland (IS)
- University of Washington (US)
- Erasmus MC (NL)
- Columbia University Irving Medical Center (US)
- University of Miami (US)
- National Institutes of Health (US)
- Cardiff University (GB)
- Brigham Young University (US)
- Corresponding author
- Goate, Alison M
- ORCID
- Huang, Kuan-lin
- Marcora, Edoardo
- Pimenova, Anna A
- Kapoor, Manav
- Jin, Sheng Chih
- Harari, Oscar
- Bertelsen, Sarah
- Fairfax, Benjamin P
- Chouraki, Vincent
- Bellenguez, Céline
- Deming, Yuetiva
- McKenzie, Andrew
- Raj, Towfique
- Renton, Alan E
- Budde, John
- Smith, Albert
- Bis, Joshua C
- DeStefano, Anita
- Adams, Hieab H H
- Ikram, M Arfan
- van der Lee, Sven
- Ibañez, Laura
- Sims, Rebecca
- Mayeux, Richard
- Haines, Jonathan L
- Farrer, Lindsay A
- Lambert, Jean Charles
- Launer, Lenore
- Seshadri, Sudha
- Williams, Julie
- Amouyel, Philippe
- Schellenberg, Gerard D
- Zhang, Bin
- Borecki, Ingrid
- Cruchaga, Carlos
- Hao, Ke
- Goate, Alison M
- Venue
- Nature Neuroscience
- Published
- 2017-06-19
- Volume
- 20
- Issue
- 8
- Pages
- 1052-1061
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC5759334
- OpenAlex
- W2634812549
- Cited by
- 423
- References
- 85
- Citations by year
- 2026: 30 · 2025: 45 · 2024: 33 · 2023: 37 · 2022: 56 · 2021: 58 · 2020: 55 · 2019: 47 · 2018: 48 · 2017: 14
- FWCI
- 33.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (201488/Z/16/Z)
- National Institute for Health and Care Research (CL-2012-13-004)
- Agence Nationale de la Recherche (ANR-11-LABX-0009)
- National Institutes of Health (5u01ag024904-10, Z99 AG999999)
- Medical Research Council (MR/L023784/2, MR/P005748/1, MR/L010305/1, MC_PC_17112, MR/L023784/1)
- National Institute on Aging (U01-AG-024904, RF1AG044546, U01AG049505, RF1AG053303, P50AG005138, R01AG035083, U01AG032984, R01AG044546, P50AG005681, P01AG003991, U01AG049508, R01AG033193, U01AG052411, RF1AG054011, U19AG024904, P01AG026276, U24AG021886)
- National Institute on Drug Abuse (R25DA027995)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, PU.1, myeloid cells, monocytes, macrophages, microglia, age of onset, genetic susceptibility loci, CELF1, transcription factor binding, CD33, MS4A4A, MS4A6A, TYROBP, phagocytosis, heritability, haplotypes
- MeSH terms
- Proto-Oncogene Protein Spi-1, Alleles, Alzheimer Disease, Animals, Female, Haplotypes, Humans, Male, Proto-Oncogene Proteins, Risk Factors, Transcription Factors, Trans-Activators, Linkage Disequilibrium, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Mice, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-01.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-01.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review