CoChat
Sign Up
Cited by 823Bioinformatics and Genomic NetworksGenomics and Chromatin DynamicsEpigenetics and DNA Methylation

Brain cell type–specific enhancer–promoter interactome maps and disease - risk association

Open access
Nott, Alexi, Holtman, Inge R., Coufal, Nicole G., Schlachetzki, Johannes C. M., Yu, Miao, Hu, Rong, Han, Claudia Z., Pena, Monique +18 more·2019·Science
DOI 10.1126/science.aay0793PMC PMC7028213
Open this paper in CoChat — free accountOpen PDF

Abstract

Linking enhancers to disease: Enhancers are genomic regions that regulate gene expression, sometimes in a cell-dependent manner. However, most of our knowledge of human brain cell–type enhancers derives from studies of bulk human brain tissue. Nott et al. examined chromatin and promoter activity in cell nuclei isolated from human brains. Genetic variants associated with brain traits and disease showed cell-specific patterns of enhancer enrichment. These data indicate that Alzheimer's disease is regulated by genetic variants within microglial cells, whereas psychiatric diseases tend to affect neurons. Science , this issue p. 1134

Integrity signals

Registry

Crossref · 10.1126/science.aay0793

Cites

118 of 118 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

24 of 27 cited sources checked · 8 supported · 1 flagged · accuracy not yet measured

  • Findingcitation

    Fine mapping produced 261 variants in credible sets.

    “To better understand AD genetics and the microglia interactome, we distinguished likely causal variants from those in linkage disequilibrium by applying fine mapping and identified 261 credible set variants (18).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [18] Kunkle BW, Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature Genetics 51, 414– 430 (2019). 30820047 PMC6463297 source

      The Results section, under “Functional interpretation of genetic variants” (page unavailable), confirms that the authors used fine mapping to identify credible sets. But it reports 345 plausible causal SNPs in the HLA-DRB1 locus alone, which contradicts a total of 261 credible-set variants. (LLM, conf=0.87)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
  • Findingown data

    Promoters of genes that mark a given cell type tended to show the matching H3K27ac profile.

    “Promoters associated with cell type signature genes preferentially exhibited corresponding H3K27ac profiles”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [13] Wang D, Comprehensive functional genomic resource and integrative model for the human brain. Science (New York, NY) 362, eaat8464 (2018). PMC6413328 30545857 source

      The Transcriptome analysis: Bulk and single cell section describes cell-type expression signatures and marker genes, while the Enhancers section describes H3K27ac profiles. Neither passage reports whether promoters of those marker genes preferentially exhibit H3K27ac profiles corresponding to the same cell type. I searched for the stated promoter–signature-gene relationship and cell-type-specific promoter acetylation; page numbers were not available in our copy. (LLM, conf=0.73)

      Experimental — accuracy not yet measured
  • Findingcitation

    In each cell type, a single promoter can connect to multiple enhancers.

    “We identified putative active promoters and enhancers in each cell type and found a one-to many relationship between promoters and enhancers (8).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [8] Mumbach MR, HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nature methods 13, 919 (2016). 27643841 PMC5501173 source

      The Main Text (page not provided) associates some HiChIP loops with active enhancers and promoters, but does not report how many enhancers connect to a promoter in each cell type. Searches for a one-to-many promoter–enhancer relationship and for multiple enhancers per promoter found no passage addressing that relationship. (LLM, conf=0.73)

      Experimental — accuracy not yet measured
  • Findingown data

    H3K27ac at promoters tracked gene expression in the matching cell type better than ATAC-seq or H3K4me3 ChIP-seq did.

    “Promoter H3K27ac signal correlated with gene expression of the corresponding cell type more closely than ATAC-seq and H3K4me3 ChIP-seq”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [12] Zhang Y, Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse. Neuron 89, 37– 53 (2016). 26687838 PMC4707064 source

      The Results section on cell-type transcriptomes reports RNA-seq expression profiles and correlations between samples, not correlations between promoter marks and expression. Searches for H3K27ac, ATAC-seq, H3K4me3, and chromatin accessibility found no passage in our copy comparing those signals. (LLM, conf=0.72)

      Experimental — accuracy not yet measured
  • Findingown data

    ATAC-seq and H3K27ac profiles from PU.1 nuclei matched closely with profiles previously reported for ex vivo microglia.

    “ATAC-seq and H3K27ac ChIP-seq profiles generated from PU.1 nuclei were highly correlated with those previously defined in ex vivo microglia”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [14] Gosselin D, An environment-dependent transcriptional network specifies human microglia identity. Science (New York, NY) 356, eaal3222 (2017). PMC5858585 28546318 source

      The “Enhancer landscape of human microglia” section reports that ATAC-seq and ChIP-seq data, including H3K27ac, were highly correlated across human individuals and mouse biological replicates. It does not report a comparison between profiles generated from PU.1 nuclei and previously defined ex vivo microglia profiles; its PU.1 ChIP-seq experiments used microglia isolated from six individuals. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
  • Classified as probably not a substantial claim. 5
  • May combine two claims. 25

  • Classified as probably not a substantial claim.

    Premisecitation

    Enhancer function requires physical contacts between enhancers and promoters in three dimensions.

    “The activity of enhancers depends on three-dimensional enhancer-promoter interactions (8)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [8] Mumbach MR, HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nature methods 13, 919 (2016). 27643841 PMC5501173 source

      The Main Text reports that certain Oct4–Smc1a chromatin loops are associated with active enhancers and promoters. It does not establish that enhancer activity depends on, or requires, those physical interactions. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Distinct sets of enhancers underlie specific gene expression patterns and allow responses unique to a cell type.

    “Unique enhancer repertoires underly particular patterns of gene expression and enable cell type-specific responses (7).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [7] Heinz S, Romanoski CE, Benner C, Glass CK, The selection and function of cell type-specific enhancers. Nature reviews Molecular cell biology 16, 144 (2015). 25650801 PMC4517609 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Super-enhancers, which are clusters of several enhancers, play a key role in activating genes that define cell identity.

    “Clusters of multiple enhancers, referred to as super enhancers, are particularly important in driving the expression of cell identity genes (6).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [6] Hnisz D, Super-enhancers in the control of cell identity and disease. Cell 155, 934– 947 (2013). 24119843 PMC3841062 source

  • May combine two claims.

    Findingown data

    Heritability for all psychiatric disorders and behavioral traits was strongly enriched in neuronal enhancers and promoters, and much less so in PsychENCODE bulk brain enhancers.

    “We found a strong enrichment of heritability for variants within neuronal enhancers and promoters for all psychiatric disorders and behavioral traits (Fig. 1D), which was substantially lower in PsychENCODE bulk brain enhancers (Fig. 1D) (13).”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [13] Wang D, Comprehensive functional genomic resource and integrative model for the human brain. Science (New York, NY) 362, eaat8464 (2018). PMC6413328 30545857 source
    • cites [17] S. m. a. methods. source

      The Methods section (‘Regulatory variant and eQTL analysis’; page unavailable) describes annotation of Alzheimer’s disease risk variants, not heritability enrichment across psychiatric disorders and behavioral traits. Searches for neuronal enhancers and promoters and PsychENCODE bulk-brain enhancers found no passage reporting the claimed comparison. (LLM, conf=0.76)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    The microglial enhancer at BIN1 is PLAC-linked to the BIN1 promoter, binds PU.1, and carries rs6733839, an AD-risk variant with the second-highest score after APOE that fine mapping marked as causal.

    “The BIN1 microglia-specific enhancer is PLAC-linked to the BIN1 promoter (Fig. 4A), binds to PU.1 (14) and contains the AD-risk variant rs6733839, which has the second highest AD-risk score after APOE and was fine-mapped as a casual variant (Fig. 3A).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [14] Gosselin D, An environment-dependent transcriptional network specifies human microglia identity. Science (New York, NY) 356, eaal3222 (2017). PMC5858585 28546318 source

  • Classified as probably not a substantial claim.

    Interpretationcitation

    Because the strongest GWAS risk allele for BIN1 lies in a microglial enhancer, further study of its function in microglia is warranted.

    “This finding that the most significant GWAS risk allele associated with BIN1 resides in a microglia-specific enhancer provides a rational for further investigation of its function in these cells (27).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [27] Crotti A, BIN1 favors the spreading of Tau via extracellular vesicles. Sci Rep 9, 9477 (2019). 31263146 PMC6603165 source

      The Results identify rs6733839 as the more statistically significant of two BIN1-associated AD risk alleles reported there. The Discussion says it lies near a PU.1 peak in human microglia and that the mouse Bin1 locus contains a microglia-specific super-enhancer; it does not establish that the human risk allele resides in a microglia-specific enhancer. The paper does point to microglia as a relevant cell type for further investigation. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    The datasets grouped by the cell type they came from and showed patterns specific to each cell type.

    “These datasets clustered according to cell type of origin and exhibited cell type-specific patterns”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Removing a microglial enhancer that carries AD-risk variants eliminated BIN1 expression in microglia, while neurons and astrocytes were unaffected.

    “Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia but not in neurons or astrocytes.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Oligodendrocyte nuclei included few precursor cells, and neuronal nuclei were a mix of excitatory and inhibitory subtypes.

    “Oligodendrocyte nuclei contain a low number of oligodendrocyte precursor cells, while neuronal nuclei represent a mixture of excitatory and inhibitory subtypes”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Across cell types, 219,509 significant unique interactions were detected, and replicates grouped by their source.

    “There were 219,509 significant unique interactions across cell types, and replicates clustered according to origin (fig. S6A, B; Table S5).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In microglia, neurons, and oligodendrocytes, 2,954 super-enhancers were found; 83% had PLAC interactions and connected to promoters with higher H3K27ac than promoters linked to ordinary enhancers.

    “We identified 2,954 super-enhancers in microglia, neurons and oligodendrocytes, of which 83% had PLAC-interactions and were linked to promoters with elevated H3K27ac levels compared to promoters linked to regular enhancers (fig. S7A, B).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Promoters linked to cell-type-enriched interactions in microglia, neurons, and oligodendrocytes matched ontologies typical of those cells, supporting PLAC maps as annotations of cell-type-specific promoter-enhancer links.

    “Active promoters linked to microglia, neuronal and oligodendrocyte enriched interactions were associated with gene ontology terms representative of each cell type, supporting the ability of the PLAC interactome to annotate cell type-specific promoter-enhancer interactions (Fig. 2C).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    Active promoters marked by H3K27ac revealed 288 human transcription factors with cell-type-specific activity, some linked to disease.

    “In addition, H3K27ac-defined active promoters identified 288 human transcription factors that were active in a cell type-specific manner (fig. S4, Table S2) (24), several of which have been associated with disease (fig. S5; Table S4).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [24] Lambert SA, The Human Transcription Factors. Cell 175, 598– 599 (2018). 30290144 source

  • May combine two claims.

    Findingown data

    In deletion lines, BIN1 expression was almost absent in PSC-derived microglia, but in PSCs, neurons, and astrocytes it matched control levels.

    “However, gene expression of BIN1 was nearly absent in the BIN1enh_del PSC-derived microglia, whereas BIN1 expression in BIN1enh_del PSCs and PSC-derived neurons and astrocytes were equivalent to BIN1control cells (Figs. 4C; S12E; Table S8).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Many super-enhancers contained GWAS risk variants and linked to cell-type-specific genes, implying that some GWAS variants influence expression through super-enhancers.

    “Many super-enhancers harbored GWAS disease-risk variants and were connected to cell type-specific genes, suggesting that a subset of GWAS variants act on super-enhancers to affect gene expression (fig. S7A, Table S6).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Of the PLAC-linked AD-risk genes, 25 were found in microglia, and 14 of those were absent from the other cell types.

    “Twenty-five of the PLAC-linked AD-risk genes were identified in microglia, of which 14 were not detected in the other cell types (Figs. 3B; S8).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Most active promoters are common across cell types, but only a small share of active enhancers are shared, suggesting that cell-type specificity resides mainly in enhancers.

    “Whereas active promoters are largely shared between cell types (Fig. 1B), a relatively small fraction of active enhancers overlap between cell types (Fig. 1C), indicating that cell type specificity is mainly captured within the enhancer repertoire.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In deletion lines, BIN1 expression was unchanged in hematopoietic stem cell precursors of microglia, indicating that microglial differentiation was not disrupted.

    “BIN1 expression was unchanged in BIN1enh_del PSC-derived microglia precursor hematopoietic stem cells, indicating that microglia derivation was unaffected (fig S12F).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Protein interaction networks showed that microglial AD-risk genes from PLAC-seq were densely connected to GWAS-assigned genes and centered on APOE, while neuronal and oligodendrocyte networks were smaller.

    “Protein-protein interaction (PPI) network analysis showed that microglia AD-risk genes identified by PLAC-seq were highly connected with GWAS-assigned genes and centered around APOE, whereas PPI networks for neurons and oligodendrocytes were smaller in scope (fig. S9C-E).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    BIN1 was sharply reduced in deletion microglia but not in neurons or astrocytes.

    “However, BIN1 was dramatically reduced in BIN1enh_del microglia and not neurons and astrocytes (Fig. 4D, E).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    AD-assigned genes in microglia linked to immune ontologies, while amyloid-beta processing ontologies appeared in neurons, microglia, and oligodendrocytes.

    “Microglia AD-assigned genes were associated with gene ontology terms for immune function, whereas gene ontology terms for amyloid-beta processing were associated with neurons, microglia and oligodendrocytes (fig. S9F).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    High H3K4me3 signal did not guarantee an interaction, which suggests PLAC-seq measures a distinct aspect of chromatin conformation.

    “A strong H3K4me3 signal did not dictate that an interaction will occur (fig. S6C), suggesting that PLAC-seq captures a unique dimension of the chromatin conformation.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisenone given

    Variation in non-coding DNA substantially contributes to phenotypic differences, yet interpreting its function is difficult.

    “Non-coding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The results change and broaden the set of genes probably affected by non-coding AD variants and point to the cell types where those effects occur.

    “These findings revise and expand the genes likely to be influenced by non-coding variants in AD and suggest the probable cell types in which they function.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Genetic variation probably affects disease-specific regulatory elements, and limited sample size may partly account for why some risk alleles did not overlap current regulatory atlases.

    “Disease-specific regulatory elements are likely to be influenced by genetic variation, which due to our limited sample size may partly explain the lack of overlap of a subset of risk alleles with the current regulatory atlases.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Cell-type-specific promoter-enhancer interactome maps allow major progress in interpreting GWAS risk alleles for neurological and psychiatric diseases and provide a new resource for that task.

    “The present studies provide evidence that identification of cell type-specific promoter-enhancer interactomes enables substantial advances in interpretation of GWAS risk alleles associated with neurological and psychiatric diseases and establish a new resource for this purpose.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    Expression profiling of control and deletion lines across PSCs and derived microglia, neurons, and astrocytes showed strong sample correlation and grouping by cell type.

    “Gene expression analysis of BIN1control and BIN1enh_del lines in PSC and PSC-derived microglia, neurons and astrocytes showed high correlation between samples, with clustering according to cell type (fig. S12C, D).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Control and BIN1 enhancer-deletion PSC lines had normal karyotypes and were differentiated into microglia, neurons, and astrocytes.

    “PSC control (BIN1control) and BIN1 enhancer deletion (BIN1enh_del) lines had normal karyotypes (fig. S10D) and were differentiated into microglia, neurons and astrocytes (Figs. 4B, S11A-D, S12A, B).”
    Quote in the paperSubstantial claim
28 of 28

  • The paper relies on this without support.

    Variation in non-coding DNA substantially contributes to phenotypic differences, yet interpreting its function is difficult.

    “Non-coding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging.”

  • The paper relies on this without support.

    It is often not known which variants are causal or in which cell types disease-risk variants act.

    “The causal variants and the specific cell type(s) in which the disease-risk variants may be active is often unclear.”

  • The paper relies on this without support.

    The enhancer-promoter interactome and the relationship between promoters and distal regulatory regions in different brain cell types are largely unknown.

    “the enhancer-promoter interactome of different brain cell types in vivo remain largely unknown.”
    “The relationship between promoters and distal regulatory regions for different cell types in the brain is largely unknown.”
3 of 3

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?

  • The functional linkage between rs6733839 and BIN1 has been well established.

    “including the two loci with the strongest associations with LOAD: the BIN1 locus for which the functional linkage between rs6733839 and BIN1 has been well established7,23”

    From PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia (2025) · cites this paper as [23]

    The paper’s BIN1 enhancer discussion (indexed under Abstract; no page supplied) places rs6733839 in a microglia-specific enhancer linked to the BIN1 promoter and reports that deleting the enhancer sharply reduced BIN1 expression in microglia. That establishes a functional link between the enhancer and BIN1, but the paper does not test the effect of rs6733839 itself; it calls for further investigation of the risk allele’s function. Thus, saying the variant’s functional linkage is well established overstates this source. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Microglia gene expression patterns are strongly correlated with late-onset Alzheimer's disease.

    “Genome -wide association meta -analysis studies (GWASs) 1,2 and interactome investigation 3 identified microglia gene expression patterns as strongly correlated with late-onset Alzheimer's disease (AD).”

    From APOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints (2023) · cites this paper as [3]

    The indexed passage on AD genetics and the microglia interactome (page unavailable) links AD-risk variants to microglia-specific enhancers and identifies 25 microglial genes linked to those variants. This supports an association between microglial gene regulation and AD risk, but it does not report that microglial gene-expression patterns are strongly correlated with late-onset AD. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • PIRs were significantly enriched for open chromatin regions detected in ATAC-seq and for active chromatin marks from ex-vivo microglia.

    “PIRs were significantly enriched for open chromatin regions detected in our ATAC-seq experiments and for active chromatin marks from ex-vivo microglia22, suggesting a potential regulatory role (Suppl. Fig 1).”

    From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates <i>RTFDC1</i> at the <i>CASS4</i> locus (2024) · cites this paper as [22]

    The paper’s interactome passage (page unavailable) reports that a subset of chromatin interactions colocalized with increased ATAC-seq and active chromatin-mark signals. Its microglia profiling passage says those signals were highly correlated with profiles previously defined in ex-vivo microglia. It does not report that PIRs were significantly enriched for the citing authors’ ATAC-seq regions or for marks from ex-vivo microglia. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • H3K27ac was only enriched in microglia among four major brain cell types at the region harboring rs6024870, suggesting this region acts specifically as an enhancer in microglia.

    “Of the four cell types, H3K27ac was only enriched in microglia, suggesting that this region acts specifically as an enhancer in microglia.”

    From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates <i>RTFDC1</i> at the <i>CASS4</i> locus (2024) · cites this paper as [22]

    The paper’s main text (page unavailable in our copy) reports H3K27ac profiling across four brain cell populations and identifies microglia-specific enhancers. It does not establish that H3K27ac was enriched only in microglia at the region harboring rs6024870; a search for that variant found no matching passage. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Multiple sporadic AD risk variants were identified in microglial transcriptional enhancer regions in humans.

    “multiple sporadic AD risk variants were identified in microglial transcriptional enhancer regions in humans11”

    From Amyloid-β induces lipid droplet-mediated microglial dysfunction via the enzyme DGAT2 in Alzheimer’s disease (2025) · cites this paper as [11]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
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., Keulen, Z., Pasillas, M. P., O’Connor, C., Nickl, C. K., Schafer, S. T., Shen, Z., Rissman, R. A., Brewer, J. B., Gosselin, D., Gonda, D. D., Levy, M. L., Rosenfeld, M. G., McVicker, G., Gage, F. H., Ren, B., & Glass, C. K.
Affiliations
  • University of California San Diego (US)
  • University Medical Center Groningen (NL)
  • Salk Institute for Biological Studies (US)
  • Ludwig Cancer Research (US)
  • Howard Hughes Medical Institute (US)
Corresponding author
  • Nott, Alexi
  • Holtman, Inge R.
  • Coufal, Nicole G.
  • Glass, Christopher K.
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2019-11-29
Volume
366
Issue
6469
Pages
1134-1139
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.aay0793
PMC
PMC7028213
OpenAlex
W2988958689
Semantic Scholar
7a5a15720dccbd1ffaefb1a3e7b92e9e1219a946
Cited by
823
References
118
Citations by year
2026: 70 · 2025: 119 · 2024: 106 · 2023: 104 · 2022: 128 · 2021: 113 · 2020: 72 · 2019: 6
FWCI
32.22
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Science (publishedVersion)
  • PubMed (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (acceptedVersion, other-oa)
  • Europe PMC (PubMed Central) (submittedVersion)
  • PubMed Central (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
Funders
  • National Institutes of Health (NS109200-01, KL2TR001444, P30 014195, S10-OD023689)
  • National Institute on Aging (AG057706-01, AG056511-01, AG062429-01, AG061060-01)
  • National Cancer Institute (P30-CA014195)
  • Netherlands Organization for Scientific Research (VENI research program)
  • Engman Foundation
  • American Heart Association / Allen Initiative in Brain Health and Cognitive Impairment
  • JPB Foundation
  • Cure Alzheimer's Fund
  • Alzheimer's Association (AARF-18-531498)
  • Cancer Research Institute (Irvington Postdoctoral Fellowship Program)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
Keywords
cell type-specific enhancers, human brain, microglia, neurons, Alzheimer's disease, mental illness, genetic variants, disease risk association
MeSH terms
Alzheimer Disease, Brain, Cells, Cultured, Chromatin, Enhancer Elements, Genetic, Humans, Nuclear Proteins, Promoter Regions, Genetic, Genetic Variation

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 2026-10-05.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.

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