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Genetics of the human microglia regulome refines Alzheimer’s disease risk loci

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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Fig. 1: Chromatin accessibility landscape in human microglia and AD predisposition.
Fig. 2: Transcriptional regulation by OCRs.
Fig. 3: Genetic regulation of chromatin accessibility in human microglia.
Fig. 4: Integration of AD etiologic landscape with genetic regulation of transcriptional and chromatin accessibility in microglia.
Fig. 5: TF binding landscape in microglia integrating AD genetics.

Data availability

The genotypes, omics data and metadata are available via the AD Knowledge Portal (https://adknowledgeportal.org). The AD Knowledge Portal is a platform for accessing data, analyses and tools generated by the Accelerating Medicines Partnership (AMP-AD) Target Discovery Program and other National Institute on Aging-supported programs to enable open-science practices and accelerate translational learning. The data, analyses and tools are shared early in the research cycle without a publication embargo on secondary use. Data are available for general research use according to the requirements for data access and data attribution (https://adknowledgeportal.org/DataAccess/Instructions). For access to content described in this paper, see https://doi.org/10.7303/syn26207321, including the derived data available for open distribution: processed ATAC-seq and RNA-seq data, OCR information and annotation, QTL summary and fine-mapping results and colocalization results.

TF motifs used in MotifBreakR analyses use JASPAR2016 (http://jaspar.genereg.net). ATAC-seq data were filtered with DAC Blacklisted/Exclusion List Regions (doi: 10.17989/ENCSR636HFF). Lopes et al. microglia eQTL data are available from 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 at https://dss.niagads.org/datasets/ng00105/). For Alasoo et al. caQTL and eQTL data, raw RNA-seq and ATAC-seq data are available from ENA (ERP020977) and EGA (EGAS00001002236), and processed read counts are available from Zenodo (doi: 10.5281/zenodo.259661). Young et al. microglia eQTL data are available under managed access from the EGA, upon approval by the Wellcome Sanger Institute Data Access Committee (https://ega-archive.org/datasets/EGAD00001005736).

Code availability

Code used throughout this study for analyses and visualization is available at https://doi.org/10.7303/syn26207321. Additional code may be available upon reasonable request from the corresponding authors.

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Acknowledgements

We thank the patients and families who donated material for these studies. We thank the computational resources and staff expertise provided by the Scientific Computing group at the Icahn School of Medicine at Mount Sinai. This study was supported by the National Institute on Aging, National Institutes of Health grants R01-AG050986 (P.R.), R01-AG067025 (P.R. and V.H.) and R01-AG065582 (P.R. and V.H.). J.B. was supported in part by Alzheimer’s Association Research Fellowship AARF-21-722200. P.D. is partially supported by NARSAD Young Investigator Grant 29683 from the Brain and Behavior Research Foundation.

Author information

Authors and Affiliations

Authors

Contributions

J.F.F., V.H. and P.R. conceived of and initiated the project. J.F.F. and P.R. designed experimental strategies for omics profiling. A.W.C., B.K., D.B, C.P.K. and V.H. provided human brain tissue dissections and biopsies. J.F.F., S.R., S.P.K. and Z.S. performed data generation. R.K., B.Z., J.B., G.E.H. and P.R. designed analytical strategies. R.K., J.B. and P.D. conducted initial bioinformatics, sample processing and QC for the omics data. R.K. and G.E.H. developed the computational scheme and performed the downstream analysis. B.Z. performed the QTL analysis. J.B. performed the TF analysis. P.D. performed the Hi-C analysis. K.G. performed ChIP analysis. J.H., K.P.L. and T.R. provided additional QTL resources. J.F.F. and P.R. supervised overall data generation. G.E.H. and P.R. supervised overall data analysis. R.K., J.F.F., B.Z., J.B., G.E.H. and P.R. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Roman Kosoy, Gabriel E. Hoffman or Panos Roussos.

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The authors declare no competing interests.

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Nature Genetics thanks Inge Holtman, Jeremy Schwartzentruber, 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 Schematic outline of data generation and integrative analyses.

The general schema of the data generation, processing and utilization in the analyses described.

Extended Data Fig. 2 FACS gating of fresh microglia.

The gating strategy for a representative sample targeting live (DAPI-) CD45+ microglia.

Extended Data Fig. 3 Comparison of human microglia RNA-seq dataset to other available microglia datasets.

Comparison of human microglia RNA-seq dataset to other available microglia datasets. We applied multidimensional scaling (MDS) to expression data processed by the standard RAPiD pipeline (https://github.com/CommonMindConsortium/RAPiD-nf/ blob/master/tutorial.md) for the two microglia transcriptomics datasets whose eQTLs were utilized in meta-eQTL analyses, as well as other relevant cell types. a) Comparisons include other brain-derived populations and b) only the three microglia and one monocyte datasets.

Extended Data Fig. 4 Comparison of caQTLs and eQTLs identified in our human microglia to relevant publicly available datasets.

a) π1 replication of caQTLs identified in our microglia data to macrophage caQTLs from Alasoo et al. 18 and brain homogenate from CommonMind 19; b) π1 replication of eQTLs identified in our microglia data to microglia eQTLs from Young et al. 7 and Lopes et al. 8, and to macrophage eQTLs from Alasoo et.al 18.

Extended Data Fig. 5 The distribution and LDsc analyses of colocalized eQTLs and caQTLs.

a) Venn diagrams represent the colocalized 1,457 OCR-Gene pairs among meta-caQTLs (green) and meta-eQTLs (red). Colocalized OCR-Gene pairs include 16 OCRs and 14 genes not significant in standalone meta-caQTLs and meta-eQTLs. b) The distribution of the distance between the OCRs and genes for the colocalized caQTLs and eQTL pairs. Mean distance is 101 kb, and median distance is 53 kb. c) Overlap between OCR-gene links identified via caQTL-eQTL colocalization method versus E-P links identified via the ABC approach. d) CaSNPs for the caQTLs colocalized with eQTLs are more likely to be located within OCRABC than in random OCRs. Red line represents the number of OCRABC regulated by colocalized caQTL, versus the distribution of OCRABC from 100 random sets of 1,457 distance-matched eQTL/caQTL pairs (P = 4.2 × 10-12, two-sided t-test). e) Enrichment of SNPs representing 95% confidence intervals for the meta-caQTL and meta-eQTL sets identified in fresh microglia via LDscore analyses using summary statistics from a set of selected GWAS studies. For each genetic variant the value utilized was the highest product between posterior probabilities from eQTL and caQTL analyses (n = 30,028). Values in dark blue represent nominally significant enrichment. The utilized GWAS studies are described in Supplementary Table 6, including the number of snps from each study. Coefficients from LD score regression (two-sided linear regression) are normalized by the per-SNP heritability (h2 /total SNPs per GWAS), with horizontal bars indicating SE (AD P = 0.036, FDR P = 0.17).

Extended Data Fig. 6 Integration of the landscape of AD etiology with genetic regulation of transcriptional and chromatin accessibility in microglia within the EPHA1-AS1 locus.

a) Local plot showing results from AD GWAS 13, eQTL analysis of EPHA1/EPHA1-AS1, and caQTL analysis of peak_188003 (P values are nominal significance estimates from two-sided logistic regression for GWAS, and mixed linear regression for QTL analyses). CLPP is the joint posterior probabilities 47 between the eSNP/caSNP PP and Jansen AD GWAS PP. Red points indicate genetic variants in the 95% credible set from statistical fine-mapping of each trait. Inset shows colocalization posterior probabilities (CLPP) for the top variant in the credible set for gene expression and chromatin accessibility. b) Visualization of EPHA1/EPHA1-AS1 locus showing open chromatin regions from four cell populations five and microglia from this study, E-P interactions (ABC), fine-mapped variants from AD (Jansen) 13, genetic regulation from eQTLs and caQTL from this study and colocalization analysis between pairs of traits (that is, AD GWAS, gene expression chromatin accessibility) using ‘coloc’ and all three traits using ‘moloc’ methods with Jansen et al. AD GWAS (PP15 > 0.5) 29.

Extended Data Fig. 7 Relationship between colocalized components and predicted functional annotation at EPHA1-AS1 locus.

Relationship between EPHA1-AS1 expression, OCR peak_188003 and rs11771145. a) Application of Causal Inference Test 48 identifies a genetic variant’s regulation on transcriptional activity of AD-implicated genes mediated by its effect on chromatin accessibility (one-sided Omnibus F-statistics). b) Correlation between the expression of EPHA1 and EPHA1-AS1, ATAC-seq signal at OCR peak_188003 and the genotype of rs11771145 (two-sided Spearman test). c) Predicted function of EPHA1-AS1 based on coexpression structure. lncHUB 31 predicts gene set annotations of every gene based on genome-wide co-expression structure and known annotations. Results are from query https://maayanlab.cloud/lnchub/?lnc=EPHA1-AS1.

Extended Data Fig. 8 Local manhattan plot of AD GWAS 13 and eQTL analysis for FIBP.

Red points indicate variants within the 95% confidence interval from statistical fine-mapping. P values are nominal significance estimates from two-sided logistic regression for GWAS and mixed linear regression for QTL analyses.

Extended Data Fig. 9 Local manhattan plot of AD GWAS 13 and eQTL analysis for LRRC25.

Red points indicate variants within the 95% confidence interval from statistical fine-mapping. P values are nominal significance estimates from two-sided logistic regression for GWAS and mixed linear regression for QTL analyses.

Extended Data Fig. 10 Local manhattan plot of AD GWAS 13, eQTL analysis for KCNN4 and caQTL for Peak_82668.

Bottom row indicates open chromatin regions in the window, and the red region indicates the target peak for caQTL analysis shown. Red points indicate variants within the 95% confidence interval from statistical fine-mapping. P values are nominal significance estimates from two-sided logistic regression for GWAS and mixed linear regression for QTL analyses.

Supplementary information

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Extended Methods, Supplementary Results and Discussion and Supplementary Figures 1–19.

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Supplementary Table 1

Supplementary Tables 1–19.

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Kosoy, R., Fullard, J.F., Zeng, B. et al. Genetics of the human microglia regulome refines Alzheimer’s disease risk loci. Nat Genet 54, 1145–1154 (2022). https://doi.org/10.1038/s41588-022-01149-1

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