The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Genetics of the human microglia regulome refines Alzheimer’s disease risk loci
Open accessPublished version of a preprint
Abstract
Microglia are brain myeloid cells that play a critical role in neuroimmunity and the etiology of Alzheimer's disease (AD), yet our understanding of how the genetic regulatory landscape controls microglial function and contributes to AD is limited. Here, we performed transcriptome and chromatin accessibility profiling in primary human microglia from 150 donors to identify genetically driven variation and cell-specific enhancer-promoter (E-P) interactions. Integrative fine-mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 new candidate risk genes (KCNN4, FIBP and LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1 as a key putative regulator of microglia expression and AD risk. This comprehensive resource capturing variation in the human microglia regulome provides insights into the etiology of neurodegenerative disease.
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“Our iMG and iAst cells were epigenetically similar to hMGs6 and other reported iMGs22 and Ast (hAst)6 cells (Extended Data Fig. 1i).”
From PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia (2025) · cites this paper as [6]
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“Recent human genetic studies revealed that Alzheimer's disease risk loci are enriched in genes expressed in myeloid cells, including microglia, the brain's resident immune cells.”
From Targeting Microglial Transcriptional Reprogramming as a Therapy Strategy for Alzheimer's Disease (2026) · cites this paper as [5]
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“Interestingly, some Alzheimer's disease risk loci are highly enriched within or near genes encoding microglia‐specific transcription factors, highlighting their crucial roles in the pathogenesis of Alzheimer's disease (Table 1) [5, 7, 20, 21].”
From Targeting Microglial Transcriptional Reprogramming as a Therapy Strategy for Alzheimer's Disease (2026) · cites this paper as [5]
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“In human microglial regulome studies, transcription factor network analyses nominated SPI1 as a key regulator of microglial gene expression and Alzheimer’s disease risk, helping connect noncoding genetic risk loci to cell-type-specific gene regulation [30].”
From Lymphoid-like Suppressive Microglia in Alzheimer’s Disease: A New Neuroimmune Regulatory Axis? (2026) · cites this paper as [30]
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Cites
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212
The International HapMap Project
†The International HapMap Consortium, Genotyping centres: Baylor College of Medicine and ParAllele BioScience, Gibbs, R. A., Belmont, J. W., Hardenbol, P., Willis, T. D., Yu, F., Chinese HapMap Consortium, Yang, H., Ch'ang, L.-Y., et al. (2003) · Nature · doi:10.1038/nature02168 · Cited by 6,057
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
Giambartolomei, 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
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653
Research works printed but not linked: 1
- Sidak, Z. Rectangular confidence regions for the means of multivariate normal distributions. J. Am. Stat. Assoc. 62, 626 (1967).
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Heneka, M. T., van der Flier, W. M., Jessen, F., Hoozemanns, J., Thal, D. R., Boche, D., Brosseron, F., Teunissen, C., Zetterberg, H., Jacobs, A. H., et al. (2025) · Nature Reviews Immunology · doi:10.1038/s41577-024-01104-7 · Cited by 638
The complex genetic architecture of Alzheimer's disease: novel insights and future directions
Andrews, S. J., Renton, A. E., Fulton-Howard, B., Podlesny-Drabiniok, A., Marcora, E., & Goate, A. M. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104511 · Cited by 377
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Epigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion
Xiong, X., James, B. T., Boix, C. A., Park, Y. P., Galani, K., Victor, M. B., Sun, N., Hou, L., Ho, L.-L., Mantero, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.040 · Cited by 205
Self-citationCell subtype-specific effects of genetic variation in the Alzheimer’s disease brain
Fujita, M., Gao, Z., Zeng, L., Aubin, C., White, C. C., Ng, B., Green, G. S., Rozenblatt–Rosen, O., Phillips, D., Amir-Zilberstein, L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01685-y · Cited by 196
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Metadata
- Authors
- Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., de Paiva Lopes, K., Charney, A. W., Kopell, B. H., Raj, T., Bennett, D., Kellner, C. P., Haroutunian, V., Hoffman, G. E., & Roussos, P.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Rush University Medical Center (US)
- James J. Peters VA Medical Center (US)
- Nathan Kline Institute for Psychiatric Research (US)
- Corresponding author
- Kosoy, Roman
- Hoffman, Gabriel E.
- Roussos, Panos
- ORCID
- Venue
- Nature Genetics
- Published
- 2022-08
- Volume
- 54
- Issue
- 8
- Pages
- 1145-1154
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC9388367
- OpenAlex
- W4295079251
- Cited by
- 168
- References
- 70
- Citations by year
- 2026: 40 · 2025: 49 · 2024: 44 · 2023: 25 · 2022: 10
- FWCI
- 11.23
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (R01-AG067025, R01-AG065582)
- Alzheimer’s Association (AARF-21-722200)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- Alzheimer's disease, microglia, enhancer-promoter interactions, chromatin accessibility, gene expression profiling, fine mapping, transcriptional regulatory networks, KCNN4, neuroimmunity, gene regulatory mechanisms
- MeSH terms
- Alzheimer Disease, Brain, Carrier Proteins, Humans, Membrane Proteins, Microglia, Neurodegenerative Diseases, Transcriptome
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