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Cited by 302Single-cell and spatial transcriptomicsGenetic Associations and EpidemiologyBioinformatics and Genomic Networks

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

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Zhang, Martin Jinye, Hou, Kangcheng, Dey, Kushal K., Sakaue, Saori, Jagadeesh, Karthik A., Weinand, Kathryn, Taychameekiatchai, Aris, Rao, Poorvi +7 more·2022·Nature Genetics
DOI 10.1038/s41588-022-01167-zPMC PMC9891382

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Abstract

Abstract Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution; scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N=341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states; subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region; and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

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  • Single-cell polygenic risk enrichment for AD risk identified a significant increase throughout the microglia trajectory, revealing an enrichment of AD genetic risk single-nucleotide polymorphisms in DAMs.

    “We performed single-cell polygenic risk enrichment for AD risk in the microglia trajectory,46,52 and identified a significant increase throughout the trajectory, revealing an enrichment of AD genetic risk single-nucleotide polymorphisms (SNPs) in DAMs (Figure 6H; STAR Methods; Data S3).”

    From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [52]

  • Microglia and immune cells showed consistently high GWAS scores across regions, with the top scores for the microglia TPT1+ subtype and macrophages in the HC, thalamus and AG.

    “Microglia and immune cells showed consistently high scores across regions, with the top scores for the microglia TPT1+ subtype and macrophages in the HC, thalamus and AG (Extended Data Fig. 9c).”

    From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [55]

  • Disease-relevance scores were calculated per cell against a recent Alzheimer's GWAS using scDRS, and overlap with microglia/immune modules was tested by hypergeometric test with Benjamini-Hochberg correction.

    “We calculated the disease-relevance score of each cell in the dataset against a recent Alzheimer’s GWAS, using scDRS (based on MAGMA)54,55,116. For the scDRS results, we counted the fraction of cells with significant scDRS scores (FDR < 0.05) in each cell type, subtype and region. To test for overlap with microglia/immune modules, we compared the set of immune cells with significant expression of”

    From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [55]

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Authors
Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., Taychameekiatchai, A., Rao, P., Pisco, A. O., Zou, J., Wang, B., Gandal, M., Raychaudhuri, S., Pasaniuc, B., & Price, A. L.
Affiliations
  • Broad Institute (US)
  • University of California, Los Angeles (US)
  • University of California, San Francisco (US)
  • Chan Zuckerberg Biohub San Francisco (US)
Corresponding author
  • Zhang, Martin Jinye
  • Hou, Kangcheng
  • Price, Alkes L.
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2022-09-01
Volume
54
Issue
10
Pages
1572-1580
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-022-01167-z
PMC
PMC9891382
OpenAlex
W4294051824
Cited by
302
References
31
Citations by year
2026: 117 · 2025: 96 · 2024: 62 · 2023: 23 · 2022: 4
FWCI
15.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • U.S. Department of Health & Human Services | National Institutes of Health (R01 MH115676, U01 HG009379, R01 MH101244, R37 MH107649, U01 HG012009, R01 HG006399, R01MH121521, R01MH123922, P01-AI148102, UC2-AR081023, R01-AR063759, UH2-AR067677)
  • U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (K99HG012203, F32 HG011434-03)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
polygenic risk, single-cell RNA sequencing, T cell subsets, inflammatory bowel disease, schizophrenia, hepatocyte subpopulations, triglyceride levels, cell ploidy, drug target genes, Mendelian disease genes, cell subpopulations
MeSH terms
RNA-Seq, Triglycerides, Multifactorial Inheritance, Gene Expression Profiling, Genome-Wide Association Study, Single-Cell Analysis

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