Abstract
Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. Mapping the genetics of gene expression in human microglia has identified several loci associated with disease-associated genetic variants in microglia-specific regulatory elements. However, identifying genetic effects on splicing is challenging because of the use of short sequencing reads. Here, we present the isoform-centric microglia genomic atlas (isoMiGA), which leverages long-read RNA sequencing to identify 35,879 novel microglia isoforms. We show that these isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ancestry meta-analysis of 555 human microglia short-read RNA sequencing samples from 391 donors, and found associations with genetic risk loci in Alzheimer’s and Parkinson’s disease. We nominate several loci that may act through complex changes in isoform and splice-site usage.
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Data availability
Datasets used in this study are publicly available as follows: long-read data at https://www.synapse.org/Synapse:syn64371963, Gaffney short-read cohort at https://ega-archive.org/datasets/EGAD00001005736, Raj short-read microglia samples at https://dss.niagads.org/datasets/ng00105/, Roussos short-read batch 1 at https://doi.org/10.7303/syn26207321, Roussos short-read batch 2 at https://www.synapse.org/Synapse:syn25671146, iPS-derived microglia short-read at Gene Expression Omnibus GSE240907, novel isoform GTF and FASTA via Zenodo at https://zenodo.org/record/8290956 (ref. 93), count matrices for all cohorts via Zenodo at https://doi.org/10.5281/zenodo.8291210 (ref. 94), all QTL summary statistics via Zenodo at https://zenodo.org/record/8250771 (ref. 95), Database of Conjoined Genes at http://metasystems.riken.jp/conjoing/, AD GWAS summary statistics48 at https://www.ebi.ac.uk/gwas/studies/GCST90027158, AD GWAS summary statistics2 at https://www.niagads.org/datasets/ng00036, PD GWAS summary statistics49 at https://research.23andme.com/collaborate/, schizophrenia GWAS summary statistics (Trubetskoy) at https://pgc.unc.edu/for-researchers/download-results/, microglia caQTLs15 at https://doi.org/10.7303/syn26207321 and bulk brain eQTLs and sQTLs (CommonMind) at https://www.synapse.org/#!Synapse:syn2759792. Source data are provided with this paper.
Code availability
The genotype quality control pipeline is available via Zenodo at https://doi.org/10.5281/zenodo.13864727 (ref. 96). The long-read RNA-seq pipeline is available via Zenodo at https://doi.org/10.5281/zenodo.13864731 (ref. 97). The QTL preparation and meta-analysis pipeline is available via Zenodo at https://doi.org/10.5281/zenodo.13864735 (ref. 98). The colocalization and MESC pipelines are available via Zenodo at https://doi.org/10.5281/zenodo.13864729 (ref. 99). All code used to produce analysis for figures is available via Zenodo at https://doi.org/10.5281/zenodo.13864720 (ref. 100).
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Acknowledgements
We thank the patients and families who donated material for these studies. We thank E. Tseng of Pacific Biosciences who provided important critical feedback on long-read analysis. This study was supported by the following National Institutes of Health grant nos: NIA U01-AG058635, NIA R21-AG063130, NIA R01-AG054005, NIA U01-AG068880, NIA RF1-AG065926, NIA R56-AG055824, NIA P30-AG066514, NINDS U54-NS123743 and NINDS R01-NS116006 to T.R., J.H., E.B., D.M., E.C. and B.Z.M. NIA U01-AG058635, NIA R01-AG067025, NIA R01-AG082185, NIA R01-AG050986, NIA R01-AG065582, NIMH R01-MH125246 and NINDS U01-NS125580 to P.R., R.K., B.Z., S.P.K., S.A., Z.S., G.E.H. and J.F.F. NIA U01-AG058635 to A.M.G., A.M. and A.G.E. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant no. UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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The study was conceived by J.H., P.R. and T.R. Data analysis was led by J.H., with contributions from E.B., R.K., B.Z., G.E.H., A.R., T.N., B.Z.M. and C.-F.T. Data generation was performed by E.C., D.M., A.G.E., E.N., A.A., G.J.L.J.S., C.D.S., V.F.-A., A.M., S.P.K., S.A., P.M., K.P., R.B.K., Z.S., N.F., C.-F.T., M.A.G., M.E.M., V.L.P., K.K.W., T.S. and J.F.F. Work was supervised by G.E.H., T.L., L.D.d.W., R.S., A.M.G., J.F.F., D.A.B., V.H., P.R. and T.R. The paper was written by J.H., with input from all co-authors.
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The following authors wish to disclose their industry relations: A.M.G. is a scientific advisory board member for Genentech and Muna Therapeutics; R.S. is currently a paid consultant and equity holder at GeneDx; N.F. is currently an employee of Pacific Biosciences; B.Z.M. is currently an employee of Abbvie; A.G.E. is an employee of BlueRock Therapeutics. All other authors declare no competing interests.
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Humphrey, J., Brophy, E., Kosoy, R. et al. Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing. Nat Genet 57, 604–615 (2025). https://doi.org/10.1038/s41588-025-02099-0
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DOI: https://doi.org/10.1038/s41588-025-02099-0
