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Identifying genetic variants that influence the abundance of cell states in single-cell data

Nature Genetics (2024)Cite this article

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Abstract

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype–Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10−11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

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Fig. 1: Method schematic.
Fig. 2: csaQTLs detected in the OneK1K dataset.
Fig. 3: Characterization of the csaQTL at 12p13.2.
Fig. 4: PRSs aggregate the effects of individual loci to highlight disease-relevant cell states, a valuable point of comparison with single-cell case-control analyses.

Data availability

The OneK1K single-cell and genotyping data are available via Gene Expression Omnibus (GEO; GSE196830). The Perez et al. genotyping data is available on DBGaP (phs002812.v1.p1) and the corresponding single-cell data is available via GEO (GSE174188). The Randolph et al. genotyping data is available on the Sequence Read Archive (PRJNA736483) and corresponding single-cell data is available on GEO (GSE162632). The Jerber et al. genotyping data is available from the European Nucleotide Archive (Project ID PRJEB11750) and the corresponding single-cell data is available on Zenodo (https://zenodo.org/records/4651413)83. The Oelen et al. single-cell data is available via the European Genome-Phenome Archive (EGAS00001005376) and genotyping data can be accessed through an application process at https://eqtlgen.org/sc/datasets/1m-scbloodnl.html.

Code availability

An open-source repository containing the implementation of GeNA can be found on GitHub (https://github.com/immunogenomics/GeNA/) and Zenodo (https://zenodo.org/doi/10.5281/zenodo.13152792)77. All code underlying our figures and tables can be found on GitHub (https://github.com/immunogenomics/GeNA-applied/) and Zenodo (https://zenodo.org/doi/10.5281/zenodo.13281284)78.

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Acknowledgements

We thank our fellow members of the Raychaudhuri Lab, as well as Y. Luo and members of the Alkes Price and Shamil Sunyaev Laboratories, for their helpful feedback. L.R. is supported by award F30AI157385 from the National Institute of Allergy and Infectious Diseases. J.B.K. is supported by award F30AI172238 from the National Institute of Allergy and Infectious Diseases. L.R., J.B.K. and K.A.L. are supported by awards T32GM144273 and T32HG002295 from the National Institute of General Medical Sciences. C.V. is supported by award T32HG01046 from the National Human Genome Research Institute. J.A.-H. is supported by EL1 National Health and Medical Research Council (NHMRC) grant no. 2018432. P.-R.L. is supported by a Burroughs Wellcome Fund Career Award at the Scientific Interfaces. J.E.P. is supported by award 1175781 from the NHMRC and a fellowship from the Fok Foundation. S.R. is supported by awards R01AR063759 and UC2AR081023 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases, U01HG012009 and R56HG013083 from the National Human Genome Research Institute, and P01AI148102 from the National Institute of Allergy and Infectious Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Per the agreement for the Oelen et al. data, we thank the participants and the staff of Lifelines DEEP DAG2+ for their collaboration. Funding for that project was provided by the European Research Council Starting Grant no. 637640. We also acknowledge the funders of the Lifelines Cohort Study, the sample collections from which the Oelen et al. project data have been derived. Finally, we are grateful to all participants in the study cohorts whose data we have analyzed in this paper. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA

    Laurie Rumker, Saori Sakaue, Yakir Reshef, Joyce B. Kang, Jose Alquicira-Hernandez, Cristian Valencia, Kaitlyn A. Lagattuta, Aparna Nathan, Po-Ru Loh & Soumya Raychaudhuri

  2. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Laurie Rumker, Saori Sakaue, Yakir Reshef, Joyce B. Kang, Jose Alquicira-Hernandez, Cristian Valencia, Kaitlyn A. Lagattuta, Annelise Mah-Som, Aparna Nathan, Po-Ru Loh & Soumya Raychaudhuri

  3. Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Laurie Rumker, Saori Sakaue, Yakir Reshef, Joyce B. Kang, Jose Alquicira-Hernandez, Cristian Valencia, Kaitlyn A. Lagattuta, Aparna Nathan & Soumya Raychaudhuri

  4. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Laurie Rumker, Joyce B. Kang, Kaitlyn A. Lagattuta, Aparna Nathan & Soumya Raychaudhuri

  5. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Laurie Rumker, Saori Sakaue, Yakir Reshef, Joyce B. Kang, Jose Alquicira-Hernandez, Cristian Valencia, Kaitlyn A. Lagattuta, Aparna Nathan, Po-Ru Loh & Soumya Raychaudhuri

  6. Translational Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia

    Seyhan Yazar, Jose Alquicira-Hernandez & Joseph E. Powell

  7. UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia

    Seyhan Yazar & Joseph E. Powell

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  1. Laurie Rumker
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Contributions

L.R. and S.R. designed and conceptualized the study. L.R. designed and implemented the algorithm and performed data analysis with input from S.S., Y.R., P.-R.L. and S.R. S.S., C.V. and A.N. contributed to genotype data processing. J.B.K., S.Y. and J.A.-H. contributed to OneK1K dataset processing. S.Y., J.A.-H. and J.E.P. provided dataset-specific expertise. L.R. and S.R. wrote the manuscript with contributions from the remaining authors S.S., Y.R., J.B.K., S.Y., J.A.-H., C.V., K.A.L., A.M.-S., A.N., J.E.P. and P.-R.L.

Corresponding author

Correspondence to Soumya Raychaudhuri.

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Competing interests

S.R. is a founder for Mestag Therapeutics and a scientific advisor for Janssen, Sonoma and Pfizer. J.E.P. is a founder of CellTellus Laboratory. The other authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Guillaume Lettre 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 representation of our approach to project a neighborhood-based phenotype into an independent dataset for testing of association replication.

We use a published reference mapping algorithm, Symphony, to project each cell from the replication dataset (blue labels) into the embedding used for construction of the nearest neighbor graph from the discovery dataset (orange labels). For each replication dataset cell, we store its distance to the 15 nearest discovery dataset cells; these represent the seed weights of this replication dataset cell in the discovery dataset neighborhoods, of which there is one per discovery dataset cell. We use diffusion in the nearest neighbor graph to obtain from these seed weights the fractional membership of each replication dataset cell within all discovery dataset neighborhoods. For each replication dataset sample, the combination of neighborhood memberships across all cells in the sample yields the fractional abundance of that sample across discovery dataset neighborhoods. Row-wise stacking these per-sample vectors into a matrix produces an estimated Neighborhood Abundance Matrix (NAM) containing the distribution of each replication dataset sample across discovery dataset neighborhoods. We can then use the stored products of the discovery dataset NAM SVD to obtain loadings for each replication dataset sample on the discovery dataset NAM-PCs, as shown. Combining the replication dataset sample loadings on the discovery dataset NAM-PCs with the fitted coefficients that define the phenotype in the discovery dataset produces an estimated phenotype value per replication dataset sample, which can be used to test for association to the allele of interest (or case-control status), controlling for relevant covariates.

Extended Data Fig. 2 Power to detect associations between simulated genotype values and real single-cell traits.

For 94 real cell-state abundance traits that vary across individuals in the OneK1K dataset, we defined simulated genotype values per individual to create true associations to these traits. By including random noise in the simulated genotypes we can use these data to quantify the fraction of true associations detected by GeNA (power) across a spectrum of noise levels. At each noise level, we show the mean and standard error of statistical power across traits for a given p-value threshold. A dashed line is shown at y = 0.05.

Extended Data Fig. 3 Illustration of 14 real cell-state abundance traits used in our non-null simulated GWAS for T cells.

For each trait in Supplementary Fig. 9, we plot the true cell-level phenotype for which we simulated associated genotypes with varying levels of noise. Each UMAP includes one dot per T cell in the OneK1K dataset. Cells that do not affect the trait are colored grey. For example, for the “CD8 Naive Program 1” trait, we used a gene expression program that varies substantially across naive CD8 + T cells. We quantified the usage of that program across all naive CD8 + T cells and defined the trait value per individual in the dataset as the mean use of that gene expression program across all naive CD8 + T cells in that individual’s sample. Therefore, for the “CD8 Naive Program 1” trait we color all cells that are not naive CD8 + T cells grey because they do not influence the trait and we color each naive CD8 + T cell by its use of the gene expression program. Cells with greater use of the program are colored deeper red and cells with less use of the program are colored deeper blue.

Extended Data Fig. 4 Characterization of the csaQTL at 15q25.1.

(a) Boxplot of sample-level phenotype values for each individual, organized by genotype at the lead SNP. (N: C/C 297, C/T 194, T/T 32) (Bold line: Q2. Box: Q1-Q3. Whisker: furthest observation within 1.5xIQR of the box.) We also show the GeNA p-value. (b) UMAP of myeloid cells colored by neighborhood-level phenotype value (that is, correlation between cell abundance and dose of alternative allele per neighborhood). (c) Violin plot of neighborhood-level phenotype value distribution within CD14+ monocytes, CD16+ monocytes and dendritic cells. (d) Heatmap of expression across neighborhoods for genes with strong correlations in expression to the csaQTL neighborhood-level phenotype. Neighborhoods are arrayed along the x-axis by phenotype value. (e) UMAP of myeloid cells colored by cell type assignment to the CD16+ monocyte cluster. We also show the Pearson’s r value between neighborhood-level phenotype values and a binary encoding of CD16+ monocyte cluster membership per cell. (f) Boxplot of cluster-based CD16+ monocyte % myeloid cells trait value per OneK1K donor by genotype. (Box and whiskers defined as in subplot a). The csaQTL lead SNP explains 6.5% of variance in this phenotype. (g) Locus zoom plot with one marker per tested SNP, genomic position along the x-axis, and GeNA p-value on the y-axis. Each SNP marker is colored by LD value relative to the lead SNP. The lead SNP is labeled with a green diamond. The BCL2A1 eQTL lead SNP and primary sclerosing cholangitis risk lead SNP are labeled with purple triangles. (h) Diagram of genotypes for the csaQTL lead SNP and colocalizing associations to molecular, tissue and organism-level traits at this locus.

Extended Data Fig. 5 Comparison of effects captured by NAM-PCs to published csaQTL associations, using approximation of flow cytometry phenotypes in scRNA-seq data.

Z-scores for published SNP associations to specific cell-state abundance phenotypes quantified using flow cytometry by Orrù et al. are shown on the x-axis. For each SNP-trait pair, a corresponding Z-score is shown on the y-axis reflecting an association test in the OneK1K dataset between genotype and the best approximation that can be captured by NAM-PCs of a cluster-based estimate of that phenotype in the OneK1K dataset (Supplementary Methods).

Extended Data Fig. 6 GeNA’s statistical power increases linearly with the number of samples included in the single-cell dataset.

We downsampled the OneK1K cohort individuals at random to 80%, 60%, 40% or 20% of the total donor count and repeated our power analysis simulation for each downsampled dataset. Here we plot statistical power by dataset size for simulated genotypes that explain 6% or 12% of variance in the associated cell-state abundance shift trait. At each cohort size, we show the mean and standard error of GeNA’s statistical power for simulated SNPs that explain 6% or 12% of phenotypic variance.

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Supplementary Tables 1–16, Figs. 1–35 and Notes 1 and 2.

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Rumker, L., Sakaue, S., Reshef, Y. et al. Identifying genetic variants that influence the abundance of cell states in single-cell data. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01909-1

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