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Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics

Nature Genetics volume 54, pages 1479–1492 (2022)Cite this article

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Abstract

Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell–disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

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Fig. 1: Approach for identifying disease-critical cell types and cellular processes by integration of single-cell profiles and human genetics.
Fig. 2: Linking immune cell types and cellular processes to immune-related diseases and blood cell traits.
Fig. 3: Linking neuron cell subsets and cellular processes to brain-related diseases and traits.
Fig. 4: Linking cell types from diverse human tissues to disease.
Fig. 5: Linking MS and AD disease-dependent and cellular process programs to MS and AD.
Fig. 6: Linking UC disease-dependent and cellular process programs to UC and IBD.
Fig. 7: Linking asthma disease-dependent and cellular process programs to asthma and lung capacity.

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Data availability

All postprocessed scRNA-seq data (except for AD; see below) are available through the original publications with PMIDs: 28091601, 33208946, 31316211, 31097668, 31042697, 31348891, 32832598, 31209336, 31604275, 33654293, 32403949 and 30355494. In addition, gene programs, enhancer–gene-linking annotations, supplementary data files and high-resolution figures are publicly available online at https://data.broadinstitute.org/alkesgroup/LDSCORE/Jagadeesh_Dey_sclinker. The AD scRNA-seq data30 are available exclusively at https://www.radc.rush.edu/docs/omics.htm per its data usage terms. This work used summary statistics from the UK Biobank study (http://www.ukbiobank.ac.uk). The summary statistics for UK Biobank used in this paper are available at https://data.broadinstitute.org/alkesgroup/UKBB. The 1000 Genomes Project Phase 3 data are available at ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/2013050. The baseline-LD annotations are available at https://data.broadinstitute.org/alkesgroup/LDSCORE. We provide a web interface to visualize the enrichment results for different programs used in our analysis at https://share.streamlit.io/karthikj89/scgenetics/www/scgwas.py.

Code availability

This work uses the S-LDSC software (https://github.com/bulik/ldsc) to process GWAS summary statistics as well as S-LDSC software and MAGMA v.1.08 (https://ctg.cncr.nl/software/magma) for post-hoc analysis. Code for constructing cell-type, disease-dependent and cellular process gene programs from scRNA-seq data and performing the healthy and disease-shared NMF can be found at https://github.com/karthikj89/scgenetics (https://doi.org/10.5281/zenodo.6516048)106. Code for processing gene programs and combining with enhancer–gene links can be found at https://github.com/kkdey/GSSG (https://doi.org/10.5281/zenodo.6513166)107.

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Acknowledgements

We thank L. Gaffney for assistance with preparing figures as well as S. Chen, C. Smillie, B. Eraslan, A. Jaiswal and the entire groups of A.L.P and A.R. for helpful scientific discussions. This work was funded through the National Institutes of Health (NIH) F32 Fellowship (to K.A.J.), NIH Pathway to Independence K99/R00 award K99HG012203 (to K.K.D), NHGRI Genomic Innovator award (R35HG011324), by Gordon and Betty Moore, the BASE Research Initiative at the Lucile Packard Children’s Hospital at Stanford University, NIH Pathway to Independence award (R00HG009917) (to J.M.E), NIH grants (nos. U01 HG009379, R01 MH101244, R37 MH107649, R01 HG006399, R01 MH115676 and R01 MH109978) to A.L.P. and Klarman Cell Observatory, HHMI, the Manton Foundation and NIH grant (no. 5U24AI118672) to A.R. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Aviv Regev

    Present address: Genentech, South San Francisco, CA, USA

  2. These authors contributed equally: Karthik A. Jagadeesh, Kushal K. Dey.

  3. These authors jointly supervised this work: Alkes L. Price, Aviv Regev.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Karthik A. Jagadeesh, Kushal K. Dey, Daniel T. Montoro, Rahul Mohan, Jesse M. Engreitz, Ramnik J. Xavier, Alkes L. Price & Aviv Regev

  2. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Karthik A. Jagadeesh, Kushal K. Dey, Steven Gazal & Alkes L. Price

  3. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Jesse M. Engreitz

  4. BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA, USA

    Jesse M. Engreitz

  5. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Alkes L. Price

  6. Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aviv Regev

Authors
  1. Karthik A. Jagadeesh
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  2. Kushal K. Dey
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  3. Daniel T. Montoro
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  4. Rahul Mohan
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  5. Steven Gazal
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  6. Jesse M. Engreitz
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  7. Ramnik J. Xavier
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  8. Alkes L. Price
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  9. Aviv Regev
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Contributions

K.A.J., K.K.D., A.L.P. and A.R. designed the study. K.A.J. and K.K.D. developed statistical methodologies and performed all computational analyses. A.L.P. and A.R. provided expert guidance and feedback on analysis and results. D.T.M. interpreted biological signals and guided K.A.J. and K.K.D. on highlighting biological insights. K.A.J. and R.M. designed and developed the web interface to visualize the results. J.M.E. provided ABC mappings. S.G. provided guidance on enhancer–gene-linking strategies. R.J.X. provided guidance on biological interpretations. K.A.J., K.K.D, A.L.P. and A.R. wrote the manuscript with detailed input from D.T.M. and feedback from all authors.

Corresponding authors

Correspondence to Karthik A. Jagadeesh, Kushal K. Dey, Alkes L. Price or Aviv Regev.

Ethics declarations

Competing interests

A.R. is a co-founder and equity holder of Celsius Therapeutics and an equity holder in Immunitas and was an SAB member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov. From 1 August 2020, A.R. has been an employee of Genentech. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Danielle Posthuma, Yukinori Okada, Rachel Brouwer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Single-cell RNA-seq datasets.

UMAP embedding of scRNA-seq profiles (dots) colored by cell type annotations from 12 datasets (labels on top).

Extended Data Fig. 2 Standardized effect sizes of immune and brain cell type programs.

Standardized effect size (τ*) (dot size) and significance (-log10(P-value), dot color) of the heritability enrichment of immune (a,b) or brain (c) cell type programs (columns) for blood cell traits (a), immune disease traits (b), or neurological/psychological related traits (c), based on SNP annotations generated with the Roadmap∪ABC-immune (a,b) or Roadmap∪ABC-brain (c) enhancer-gene linking strategy. Numerical results are reported in Supplementary Data 1. Details for all traits analyzed are in Supplementary Table 2.

Extended Data Fig. 3 Linking cell type programs to diseases and traits across all analyzed tissues.

Magnitude (E-score, dot size) and significance (-log10(P-value), dot color) of the heritability enrichment of cell type programs (columns) from each of nine tissues (color code, legend) for GWAS summary statistics of diverse traits and diseases (rows), based on the Roadmap∪ABC enhancer-gene linking strategy for the corresponding tissue. Details for all traits analyzed are in Supplementary Table 2. See Data Availability for higher resolution version of this figure.

Extended Data Fig. 4 Cross trait analysis of cell type enrichments.

Pearson correlation coefficient (colorbar) between the cell type enrichment profiles of each pair of traits (rows, columns), clustered (dashed lines) hierarchically. Trait clusters labeled by their overall cell type enrichments.

Extended Data Fig. 5 Linking cellular process programs to relevant diseases and traits in each of six tissues.

Magnitude (E-score, dot size) and significance (-log10(P-value), dot color) of the heritability enrichment of cellular process programs (columns; obtained by NMF) in each of seven tissues (label on top) for traits relevant in that tissue (rows) using the Roadmap∪ABC strategy for the corresponding tissue. Details for all traits analyzed are in Supplementary Table 2.

Extended Data Fig. 6 Analysis of cell type programs using a non-tissue-specific enhancer-gene linking strategy.

Magnitude (E-score, dot size) and significance (-log10(P-value), dot color) of the heritability enrichment of immune (a), brain (b), lung (c), heart (d), colon (e), adipose (f) and skin (g) cell type programs (columns) for traits relevant in that tissue (rows) using a non-tissue-specific Roadmap∪ABC strategy. Details for all traits analyzed are in Supplementary Table 2.

Extended Data Fig. 7 Disease-dependent programs have low correlations with healthy and disease cell type programs.

Pearson correlation coefficient (color bar) of gene program membership vectors between healthy cell type, disease cell type and disease-dependent programs in scRNA-seq studies from a disease tissue (label on top) and the corresponding healthy tissue.

Extended Data Fig. 8 Disease specificity of disease-dependent programs.

Proportion of disease-dependent programs with a -log10(P-value) of enrichment score (p.E-score) > 3 in IBD, MS and asthma GWAS summary statistics (column) for disease-dependent programs from IBD, MS and asthma (columns), when combined with tissue-specific Roadmap∪ABC (row).

Extended Data Fig. 9 Analysis of disease-dependent programs using alternative Roadmap∪ABC enhancer-gene linking strategies.

Magnitude (E-score, dot size) and significance (-log10(P-value), dot color) of the heritability enrichment of disease-dependent programs (columns) in UC (colon cells) using Roadmap∪ABC-immune (a), asthma (lung cells) using Roadmap∪ABC-immune (b), and MS (brain cells) using Roadmap∪ABC-brain (c). Details for all traits analyzed are in Supplementary Table 2.

Extended Data Fig. 10 Analysis of disease-dependent programs across all tissues and traits.

Magnitude (E-score, dot size) and significance (-log10(P-value), dot color) of the heritability enrichment of disease-dependent programs (columns) from UC, MS, Alzheimer’s, asthma and pulmonary fibrosis (labels on top, color code, legend), for GWAS summary statistics of diverse traits and diseases (rows), based on the Roadmap∪ABC enhancer-gene linking strategy for the corresponding tissue. Details for all traits analyzed are in Supplementary Table 2. See Data Availability for higher resolution version of this figure.

Supplementary information

Supplementary Information

Supplementary Note, Tables 1 and 2, and Figs. 1–7.

Reporting Summary

Supplementary Data 1

Healthy cell-type program heritability enrichment results. Numerical values for E-score and significance are reported for all cell-type programs and traits analyzed.

Supplementary Data 2

Disease-dependent program heritability enrichment results. Numerical values for E-score and significance are reported for all disease-dependent programs and traits analyzed.

Supplementary Data 3

Cellular process program heritability enrichment results. Numerical values for E-score and significance are reported for all healthy, disease and shared cellular processes and traits analyzed.

Supplementary Data 4

List of genes driving each enrichment. Up to 50 genes with the strongest MAGMA gene score and membership in the gene program.

Supplementary Data 5

Heritability enrichment results from eQTL, PCHi-C and other alternative enhancer–gene-linking strategies. Numerical values for E-score and significance are reported for all traits analyzed with alternative enhancer–gene-linking strategies.

Supplementary Data 6

Heritability enrichment results from alternative approaches for constructing cell-type gene programs. Numerical values for E-score and significance are reported for all traits analyzed with the alternative cell-type programs.

Supplementary Data 7

MAGMA analysis with alternative input representations. Sensitivity/specificity index, s.e.m., average sensitivity and average specificity for various binarization thresholds (0.20–0.95) and continuous variable approaches (probability scale or −log(odds) of the probability scale), for the analysis of both five blood cell traits and four major categories of diseases/traits.

Supplementary Data 8

FUMA enrichments for blood cell traits and immune cell-type programs. Numerical values for beta, s.e.m. and P value for all cell types and traits analyzed.

Supplementary Data 9

MAGMA GSEA results for all cell-type programs. MAGMA scores across all traits analyzed.

Supplementary Data 10

Pathway enrichment analysis for each disease-dependent program. Gene overlap, P value and gene list for each of the enriched pathway ontology terms across KEGG, Wikipathways and Reactome.

Supplementary Data 11

Composition of cell types in each tissue. Proportion of cells observed for each cell type and condition in each of the single-cell datasets.

Supplementary Data 12

Correlation between disease-dependent and healthy cell-type program.

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Jagadeesh, K.A., Dey, K.K., Montoro, D.T. et al. Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat Genet 54, 1479–1492 (2022). https://doi.org/10.1038/s41588-022-01187-9

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