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Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies

Nature Genetics volume 54, pages 4–17 (2022)Cite this article

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Abstract

Microglia have emerged as important players in brain aging and pathology. To understand how genetic risk for neurological and psychiatric disorders is related to microglial function, large transcriptome studies are essential. Here we describe the transcriptome analysis of 255 primary human microglial samples isolated at autopsy from multiple brain regions of 100 individuals. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region and aging. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, finding associations with microglial expression of USP6NL for Alzheimer’s disease and P2RY12 for Parkinson’s disease. We have built the most comprehensive catalog to date of genetic effects on the microglial transcriptome and propose candidate functional variants in neurological and psychiatric disorders.

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Fig. 1: Overview of the MiGA.
Fig. 2: Regional heterogeneity analysis.
Fig. 3: Age-related analysis.
Fig. 4: Genetic regulatory effects in microglia.
Fig. 5: Summary of colocalization analyses.
Fig. 6: Enhancer–promoter interaction data link GWAS variants to microglia-specific regulatory regions.
Fig. 7: sQTLs in CD33 and MS4A6A colocalize with the AD risk loci.

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Data availability

Raw and processed RNA-seq and genotype datasets have been deposited in the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) at https://dss.niagads.org/datasets/ng00105/ under accession no. NG00105.v1). The user will need to log on the NIAGADS Data Access Request to start an application. Instructions to download the dataset can be found at https://www.niagads.org/data/request/data-request-instructions. All differential expression, gene lists and fine-mapping results are presented as supplementary tables. The GWAS fine-mapping results are available from the echolocatoR Shiny application at https://rajlab.shinyapps.io/Fine_Mapping_Shiny. Full nominal and permuted eQTL and sQTL summary statistics per brain region are available from Zenodo at https://doi.org/10.5281/zenodo.4118605 (eQTL) and https://doi.org/10.5281/zenodo.4118403 (sQTL). Results for the eQTL and sQTL meta-analyses (mashR and METASOFT) and colocalization (COLOC) are available from Zenodo at https://doi.org/10.5281/zenodo.4118676.

Code availability

All the code used to perform the analysis is available at https://github.com/RajLabMSSM/MiGA_public_release. To perform eQTL mapping, we followed the latest pipeline created by the GTEx consortium101 (https://github.com/broadinstitute/gtex-pipeline). To estimate and compare the genetic effects in gene expression and splicing proportions across different brain regions, we used the mashR pipeline40 (https://stephenslab.github.io/gtexresults/gtex.html). The tools used for genotyping quality control or specific R packages are described in the Methods and Supplementary Note.

Change history

  • 18 January 2022

    In the version of this article initially published online, the link for Supplementary Tables 1–23 was missing and has been restored as of 18 January 2022.

References

  1. Priller, J. & Prinz, M. Targeting microglia in brain disorders. Science 365, 32–33 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Ransohoff, R. M. & El Khoury, J. Microglia in health and disease. Cold Spring Harb. Perspect. Biol. 8, a020560 (2015).

    Article  PubMed  Google Scholar 

  3. Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Tan, Y.-L., Yuan, Y. & Tian, L. Microglial regional heterogeneity and its role in the brain. Mol. Psychiatry 25, 351–367 (2020).

    Article  PubMed  Google Scholar 

  5. van der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. De Biase, L. M. et al. Local cues establish and maintain region-specific phenotypes of basal ganglia microglia. Neuron 95, 341–356.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Soreq, L. et al. Major shifts in glial regional identity are a transcriptional hallmark of human brain aging. Cell Rep. 18, 557–570 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 599 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. Masuda, T. et al. Author correction: spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 568, E4 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Galatro, T. F. et al. Transcriptomic analysis of purified human cortical microglia reveals age-associated changes. Nat. Neurosci. 20, 1162–1171 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. McGeer, P. L. et al. Microglia in degenerative neurological disease. Glia 7, 84–92 (1993).

    Article  CAS  PubMed  Google Scholar 

  18. Kreutzberg, G. W. Microglia: a sensor for pathological events in the CNS. Trends Neurosci. 19, 312–318 (1996).

    Article  CAS  PubMed  Google Scholar 

  19. Trépanier, M. O., Hopperton, K. E., Mizrahi, R., Mechawar, N. & Bazinet, R. P. Postmortem evidence of cerebral inflammation in schizophrenia: a systematic review. Mol. Psychiatry 21, 1009–1026 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hopperton, K. E., Mohammad, D., Trépanier, M. O., Giuliano, V. & Bazinet, R. P. Markers of microglia in post-mortem brain samples from patients with Alzheimer’s disease: a systematic review. Mol. Psychiatry 23, 177–198 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. McCauley, M. E. & Baloh, R. H. Inflammation in ALS/FTD pathogenesis. Acta Neuropathol. 137, 715–730 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53, 861–868 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Masuda, T., Sankowski, R., Staszewski, O. & Prinz, M. Microglia heterogeneity in the single-cell era. Cell Rep. 30, 1271–1281 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Raj, T. et al. CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility. Hum. Mol. Genet. 23, 2729–2736 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hoffman, G. E. & Roussos, P. Dream: powerful differential expression analysis for repeated measures designs. Bioinformatics 37, 192–201 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Hartigan, J. A. & Wong, M. A. A K-means clustering algorithm. J. R. Stat. Soc. Ser. C Appl. Stat. 28, 100–108 (1979).

    Google Scholar 

  33. Srinivasan, K. et al. Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep. 31, 107843 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat. Commun. 10, 994 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019).

    Article  CAS  PubMed  Google Scholar 

  41. Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Navarro, E. et al. Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells. Nat. Aging 1, 850–863 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Storey, J. D. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Stat. 31, 2013–2035 (2003).

    Article  Google Scholar 

  45. Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 88, 586–598 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  CAS  PubMed Central  Google Scholar 

  52. Patsopoulos, N. A. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).

    Article  CAS  Google Scholar 

  53. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Desikan, R. S. et al. Polygenic overlap between C-reactive protein, plasma lipids, and Alzheimer disease. Circulation 131, 2061–2069 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. Nott, A. et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Grenn, F. P. et al. The Parkinson’s disease genome-wide association study locus browser. Mov. Disord. 35, 2056–2067 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  58. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Böttcher, C. et al. Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat. Neurosci. 22, 78–90 (2019).

    Article  PubMed  Google Scholar 

  60. Mittelbronn, M., Dietz, K., Schluesener, H. J. & Meyermann, R. Local distribution of microglia in the normal adult human central nervous system differs by up to one order of magnitude. Acta Neuropathol. 101, 249–255 (2001).

    Article  CAS  PubMed  Google Scholar 

  61. Olah, M. et al. Identification of a microglia phenotype supportive of remyelination. Glia 60, 306–321 (2012).

    Article  PubMed  Google Scholar 

  62. Stevens, B. et al. The classical complement cascade mediates CNS synapse elimination. Cell 131, 1164–1178 (2007).

    Article  CAS  PubMed  Google Scholar 

  63. Badimon, A. et al. Negative feedback control of neuronal activity by microglia. Nature 586, 417–423 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Savage, J. C. et al. Nuclear receptors license phagocytosis by TREM2+ myeloid cells in mouse models of Alzheimer’s disease. J. Neurosci. 35, 6532–6543 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Courtney, R. & Landreth, G. E. LXR regulation of brain cholesterol: from development to disease. Trends Endocrinol. Metab. 27, 404–414 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kao, Y.-C., Ho, P.-C., Tu, Y.-K., Jou, I.-M. & Tsai, K.-J. Lipids and Alzheimer’s disease. Int. J. Mol. Sci. 21, 1505 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  67. Proitsi, P. et al. Alzheimer’s disease susceptibility variants in the MS4A6A gene are associated with altered levels of MS4A6A expression in blood. Neurobiol. Aging 35, 279–290 (2014).

    Article  CAS  PubMed  Google Scholar 

  68. Huang, K.-L. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat. Neurosci. 20, 1052–1061 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Deming, Y. et al. The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk. Sci. Transl. Med. 11, eaau2291 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Novikova, G. et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat. Commun. 12, 1610 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Mildner, A., Huang, H., Radke, J., Stenzel, W. & Priller, J. P2Y12 receptor is expressed on human microglia under physiological conditions throughout development and is sensitive to neuroinflammatory diseases. Glia 65, 375–387 (2017).

    Article  PubMed  Google Scholar 

  72. Tóth, A., Antal, Z., Bereczki, D. & Sperlágh, B. Purinergic signalling in Parkinson’s disease: a multi-target system to combat neurodegeneration. Neurochem. Res. 44, 2413–2422 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  73. van Wageningen, T. A. et al. Regulation of microglial TMEM119 and P2RY12 immunoreactivity in multiple sclerosis white and grey matter lesions is dependent on their inflammatory environment. Acta Neuropathol. Commun. 7, 206 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Haynes, S. E. et al. The P2Y12 receptor regulates microglial activation by extracellular nucleotides. Nat. Neurosci. 9, 1512–1519 (2006).

    Article  CAS  PubMed  Google Scholar 

  75. Marsh, S. E. et al. Single cell sequencing reveals glial specific responses to tissue processing & enzymatic dissociation in mice and humans. Preprint at bioRxiv https://doi.org/10.1101/2020.12.03.408542 (2020).

  76. Mattei, D. et al. Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations. Int. J. Mol. Sci. 21, 7944 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  77. Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Ramdhani, S. et al. Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs. PLoS Genet. 16, e1008549 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. de Lange, G. M., Rademaker, M., Boks, M. P. & Palmen, S. J. M. C. Brain donation in psychiatry: results of a Dutch prospective donor program among psychiatric cohort participants. BMC Psychiatry 17, 347 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Melief, J. et al. Characterizing primary human microglia: a comparative study with myeloid subsets and culture models. Glia 64, 1857–1868 (2016).

    Article  CAS  PubMed  Google Scholar 

  81. Sneeboer, M. A. M. et al. Microglia in post-mortem brain tissue of patients with bipolar disorder are not immune activated. Transl. Psychiatry 9, 153 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Shah, H. PgmNr 1856: RAPiD—An Agile and Dependable RNA-Seq Framework (American Society of Human Genetics, 2015).

  83. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  84. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).

    Article  PubMed  Google Scholar 

  86. Auton, A. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  Google Scholar 

  87. Boettger, L. M., Handsaker, R. E., Zody, M. C. & McCarroll, S. A. Structural haplotypes and recent evolution of the human 17q21.31 region. Nat. Genet. 44, 881–885 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Allcock, R. J. N. et al. The MHC haplotype project: a resource for HLA-linked association studies. Tissue Antigens 59, 520–521 (2002).

    Article  CAS  PubMed  Google Scholar 

  89. Li, H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Schilder, B. M., Humphrey, J. & Raj, T. echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline. Bioinformatics https://doi.org/10.1093/bioinformatics/btab658 (2021).

  91. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  92. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Patir, A., Shih, B., McColl, B. W. & Freeman, T. C. A core transcriptional signature of human microglia: derivation and utility in describing region-dependent alterations associated with Alzheimer’s disease. Glia 67, 1240–1253 (2019).

    Article  PubMed  Google Scholar 

  95. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Fort, A. et al. MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets. Bioinformatics 33, 1895–1897 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  101. Aguet, F. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  CAS  Google Scholar 

  102. Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Taylor-Weiner, A. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol. 20, 228 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Cotto, K. C. et al. RegTools: integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer. Preprint at bioRxiv https://doi.org/10.1101/436634 (2021).

  105. Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).

    Article  CAS  PubMed  Google Scholar 

  106. Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017).

    PubMed  Google Scholar 

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Acknowledgements

We thank members of the Raj and de Witte labs for their feedback on the manuscript. We thank the teams of the NBB and Mount Sinai Neuropathology Brain Bank and Research CoRE for their services. We thank the study participants for their generous gifts of brain donation. Microglia were isolated through the efforts of a large team and we thank M. Litjens, R. D. van Dijk, A. Fernández-Andreu, P. R. Ormel, H. C. van Mierlo, Y. He, S. Gumbs, M. E van Strien, S. Burm, V. Donega and E. M. Hol for all their contributions to this effort. We thank M. Chao for his assistance with genotyping quality control. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. The research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health (NIH) under award no. S10OD026880. T.R. is supported by grants from the NIH (nos. NIA R21-AG063130, NIA R01-AG054005, NIA U01-AG068880, NIA RF1-AG065926, NIA R56-AG055824 and NINDS R01-NS116006). G.S. was supported through ZonMw and the foundation ‘De Drie Lichten’ in the Netherlands. E.N. was supported by a Ramon Areces fellowship. The funders had no role in study design, data collection and analysis, decision to publish or manuscript preparation.

Author information

Author notes

  1. These authors contributed equally Katia de Paiva Lopes, Gijsje J. L. Snijders, Jack Humphrey, Lot D. de Witte, Towfique Raj.

Authors and Affiliations

  1. Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Katia de Paiva Lopes, Jack Humphrey, Amanda Allan, Elisa Navarro, Brian M. Schilder, Ricardo A. Vialle, Madison Parks, Emily M. Hicks & Towfique Raj

  2. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Katia de Paiva Lopes, Jack Humphrey, Amanda Allan, Elisa Navarro, Brian M. Schilder, Ricardo A. Vialle, Madison Parks, Emily M. Hicks & Towfique Raj

  3. Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Katia de Paiva Lopes, Jack Humphrey, Amanda Allan, Elisa Navarro, Brian M. Schilder, Ricardo A. Vialle, Madison Parks, Emily M. Hicks & Towfique Raj

  4. Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Katia de Paiva Lopes, Jack Humphrey, Amanda Allan, Elisa Navarro, Brian M. Schilder, Ricardo A. Vialle, Madison Parks, Emily M. Hicks & Towfique Raj

  5. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Gijsje J. L. Snijders, Roy Missall, Welmoed van Zuiden, Frederieke A. J. Gigase, Raphael Kübler, René S. Kahn & Lot D. de Witte

  6. Mental Illness Research, Education and Clinical Center, James J Peters VA Medical Center, New York, NY, USA

    Gijsje J. L. Snijders, Frederieke A. J. Gigase, René S. Kahn & Lot D. de Witte

  7. Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands

    Marjolein A. M. Sneeboer & Amber Berdenis van Berlekom

  8. Department of Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany

    Chotima Bӧttcher & Josef Priller

Authors
  1. Katia de Paiva Lopes
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  2. Gijsje J. L. Snijders
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  3. Jack Humphrey
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  4. Amanda Allan
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  5. Marjolein A. M. Sneeboer
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  6. Elisa Navarro
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  7. Brian M. Schilder
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  8. Ricardo A. Vialle
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  9. Madison Parks
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  10. Roy Missall
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  11. Welmoed van Zuiden
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  12. Frederieke A. J. Gigase
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  13. Raphael Kübler
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  14. Amber Berdenis van Berlekom
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  15. Emily M. Hicks
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  16. Chotima Bӧttcher
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  17. Josef Priller
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  18. René S. Kahn
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  19. Lot D. de Witte
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  20. Towfique Raj
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Contributions

L.D.d.W. and T.R. conceived and supervised the study. G.J.L.S., M.A.M.S. and A.B.v.B. isolated the microglia at University Medical Center Utrecht. G.J.L.S., E.N., A.A., M.P., F.A.J.G. and R.K. isolated the microglia at Mount Sinai School of Medicine. E.N., A.A. and M.P. performed genotyping and RNA-seq. W.v.Z. performed RNA-seq on stimulated microglia samples with input from G.J.L.S. and L.D.d.W. K.P.L. performed the data preprocessing and quality control. K.P.L. led the analyses of the region, aging, QTL analyses and meta-analysis, with input from J.H. and G.J.L.S. G.J.L.S. led data interpretation, functional overlaps and replication work. J.H. led the genetic, fine-mapping and epigenomic analyses. B.M.S. assisted with the fine-mapping analyses. R.A.V. assisted with QTL mapping and performed genotyping quality control. E.M.H. performed the single-cell analysis. R.S.K. provided funding and was involved in establishing the NBB for Psychiatry, providing tissue for this project. R.M. performed the validation work. J.P. and C.B. provided data for validation. J.H., G.J.L.S., K.P.L., L.D.W. and T.R. wrote the manuscript with input from all coauthors. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Lot D. de Witte or Towfique Raj.

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The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Yi Xing and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Regional heterogeneity analysis for transcript usage.

A) Heatmap of relative transcript usage between regions using all 176 transcripts from pairwise comparisons of differential transcript usage (DTU; empirical FDR < 0.1), plotted as row-scaled z-scores of mean transcript usage per region; red and blue indicates high and low relative transcript usage, respectively. Transcripts form 2 k-means clusters, n refers to the number of transcripts in each cluster. Core microglia genes from Patir et al. highlighted. B) Transcript usage plots for the gene RGS1. The two most abundant transcripts are bolded. The DTU signal is driven by a reduction of the intron retention transcript ENST00000498352.1 and a corresponding increase in the protein-coding transcript ENST00000367459.8 in the SVZ compared to the other regions. Boxplots show the median with the first and third quartiles of the distribution. C) Functional Enrichment Analysis of all 132 genes with regional DTU using Ingenuity Pathway Analysis (IPA). Significantly enriched terms shown (q-value < 0.05).

Extended Data Fig. 2 Age-related analysis for transcript usage.

A) Heatmap of the 225 transcripts associated with age (empirical FDR < 0.1). Each row plotted as Z-score of median expression averaged first by donor (across multiple regions) and then by age quintiles with 20 donors each. Transcripts are ordered by Ward’s hierarchical clustering. Core microglia genes from Patir et al. highlighted. B) Example transcript usage for P2RY12. The association is caused by an increase in the long protein-coding transcript ENST00000302632.3 and a corresponding decrease in the short intron retention transcript ENST00000468596.1 during aging. C) Functional Enrichment Analysis of all 150 genes with DTU in aging using Ingenuity Pathway Analysis (IPA). Only significantly enriched terms shown (q-value < 0.05).

Extended Data Fig. 3 Full colocalization results in Alzheimer’s Disease.

Colocalization PP4 displayed for each GWAS locus (right text) and gene (left text) for each QTL dataset. An empty value means no QTL was present for testing for that gene in that dataset.

Extended Data Fig. 4 Colocalization results for each regional microglia dataset in Alzheimer’s Disease.

Colocalization PP4 displayed for each GWAS locus (right text) and gene (left text) for each QTL dataset. An empty value means no QTL was present for testing for that gene in that dataset.

Extended Data Fig. 5 Full colocalization results in Parkinson’s Disease.

Colocalization PP4 displayed for each GWAS locus (right text) and gene (left text) for each QTL dataset. An empty value means no QTL was present for testing for that gene in that dataset.

Extended Data Fig. 6 Colocalization results for each regional microglia dataset in Parkinson’s Disease.

Colocalization PP4 displayed for each GWAS locus (right text) and gene (left text) for each QTL dataset. An empty value means no QTL was present for testing for that gene in that dataset.

Extended Data Fig. 7 Overlap of colocalized microglia eQTLs with epigenomic features in AD and PD.

Cell-type specific promoters and enhancers56 were overlapped with SNP sets for each colocalizing microglia QTL - GWAS locus. SNP sets consisted of the lead GWAS SNP, the lead QTL SNP and any fine-mapped consensus or credible SNPs. Results are summarized here by the number of SNPs in the set that overlap with a particular feature type.

Supplementary information

Supplementary Information

Supplementary Note, Methods, Results, Figs. 1–20.

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Supplementary Tables 1–23.

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Lopes, K.d.P., Snijders, G.J.L., Humphrey, J. et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet 54, 4–17 (2022). https://doi.org/10.1038/s41588-021-00976-y

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