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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.

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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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