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Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

Nature Genetics volume 53, pages 1322–1333 (2021)Cite this article

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Abstract

The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.

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Fig. 1: Cell-fraction-adjusted eQTLs of human kidney samples.
Fig. 2: Cell-type-dependent activities of genetic variants on gene expression.
Fig. 3: Single-cell resolution regulatory maps for the human kidney.
Fig. 4: Single-cell annotation highlights cell type convergence of kidney endophenotypes.
Fig. 5: Comprehensive gene prioritization provides new mechanistic insights into kidney function and blood pressure regulation.
Fig. 6: Multi-omic integrative annotation highlights the therapeutic targets for CKD and hypertension.

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Data availability

The eQTL data are publicly available online at the Susztak laboratory (http://susztaklab.com/eQTLci/index.php) and figshare (https://doi.org/10.6084/m9.figshare.14718015.v1). The RNA-seq and human kidney snATAC-seq data have been deposited with the Gene Expression Omnibus (GEO) under accession nos. GSE173343, GSE115098 and GSE172008, respectively. Human kidney snATAC-seq clustering and Integrative Genomics Viewer visualization are publicly available at http://susztaklab.com/HumanKidneysnATAC/ and http://susztaklab.com/human_kidney/igv/, respectively. Mouse kidney scRNA-seq data are available at https://susztaklab.com/VisCello/. No consent was obtained to share individual-level genotype data. There is no mechanism to obtain consent since tissue was collected as medical discard and the samples were permanently de-identified. Formatted summary statistics data used for LDSC were downloaded from the LDSC website (https://alkesgroup.broadinstitute.org/sumstats_formatted/ and https://alkesgroup.broadinstitute.org/UKBB/). BED-formatted baseline data v.1.1 used for LDSC were downloaded from the LDSC website (https://alkesgroup.broadinstitute.org/LDSCORE/). Park et al.20 single-cell RNA-seq data of mouse kidney were downloaded from the GEO (accession no. GSE107585). Young et al.33 single-cell RNA-seq data of human kidney were downloaded from the supplementary data 1 of Young et al.33 Wilson et al.18 single-nucleus RNA-seq data of the human kidney were downloaded from the GEO (accession no. GSE131882). The human kidney ChIP–seq data were downloaded from the GEO (accession nos. GSM621634, GSM621648, GSM621651, GSM670025, GSM772811 and GSM1112806). Source data are provided with this paper.

Code availability

The Perl and R codes used to analyze the RNA-seq, genotype, eQTL(cf), eQTL(ci) and snATAC-seq data are available at https://github.com/shengxin321/HumanKidney_eQTL_and_snATAC-seq.

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Acknowledgements

We thank the Molecular Pathology and Imaging Core (no. P30-DK050306 to K.S.) and Diabetes Research Center (no. P30-DK19525 to K.S.) at the University of Pennsylvania for their services. This work in the Susztak laboratory was supported by the National Institutes of Health (NIH grant nos. R01 DK105821, R01 DK087635 and R01 DK076077 to K.S.) and by the Foundation of the NIH Type 2 Diabetes Accelerated Medicine Partnership Project to K.S.

Author information

Author notes

  1. These authors contributed equally: Xin Sheng, Yuting Guan.

Authors and Affiliations

  1. Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA

    Xin Sheng, Yuting Guan, Ziyuan Ma, Junnan Wu, Hongbo Liu, Chengxiang Qiu, Steven Vitale, Zhen Miao, Matthew J. Seasock & Katalin Susztak

  2. Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA

    Xin Sheng, Yuting Guan, Ziyuan Ma, Junnan Wu, Hongbo Liu, Chengxiang Qiu, Steven Vitale, Zhen Miao, Matthew J. Seasock, Benjamin F. Voight & Katalin Susztak

  3. Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA

    Xin Sheng, Yuting Guan, Ziyuan Ma, Junnan Wu, Hongbo Liu, Chengxiang Qiu, Steven Vitale, Zhen Miao, Matthew J. Seasock, Benjamin F. Voight, Christopher D. Brown & Katalin Susztak

  4. Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    Matthew Palmer

  5. Merck & Co., Inc, Kenilworth, NJ, USA

    Myung K. Shin

  6. Eli Lilly and Company Lilly Corporate Center, Indianapolis, IN, USA

    Kevin L. Duffin

  7. Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA

    Steven S. Pullen

  8. Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    Todd L. Edwards & Jacklyn N. Hellwege

  9. Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Adriana M. Hung

  10. Department of Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Mingyao Li

  11. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA

    Benjamin F. Voight

  12. Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA

    Benjamin F. Voight

  13. Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore

    Thomas M. Coffman

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  1. Xin Sheng
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Contributions

K.S., X.S. and Y.G. conceived, planned and oversaw the study and wrote the manuscript. Y.G. performed the CRISPR–Cas9 medicated genome editing. X.S. and S.V. developed the Web database. Z. Ma and J.W. conducted the human kidney snATAC-seq experiment. X.S. analyzed data with the help of Y.G., H.L., C.Q., Z. Miao and S.V. Y.G., M.J.S., M.P., M.K.S., K.L.D., S.S.P., T.L.E., J.N.H., A.M.H., M.L., B.F.V., T.M.C., C.D.B. and K.S. assisted with data generation and manuscript revision.

Corresponding author

Correspondence to Katalin Susztak.

Ethics declarations

Competing interests

The Susztak laboratory receives funding from GlaxoSmithKline, Regeneron Pharmaceuticals, Gilead Sciences, Merck Sharp & Dohme, a subsidiary of Merck & Co., Boehringer Ingelheim, Bayer and Novo Nordisk. The funders had no influence on the data analysis. K.S. serves on the scientific advisory board of Jnana Therapeutics. The other authors declare no competing interests.

Additional information

Peer review information Nature Genetics thank Benjamin Humphreys and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Experimental scheme of cell type-specific GWAS trait heritability enrichment analysis.

Here we applied MAGMA to the scRNA-seq data and LDSC to the scRNA-seq and snATAC-seq data to assess the GWAS per-SNP heritability enrichment.

Extended Data Fig. 2 Experimental scheme.

a) We used Bayesian colocalization which combined information from eQTL(cf)s to annotate 264 eGFR associated loci and prioritized 182 causal genes for kidney disease, where the causal variants for gene expression and kidney function were shared. b) Experimental scheme. We used Bayesian colocalization, which combined information from eQTL(cf)s to annotate 340 SBP GWAS associated loci, and prioritized 88 causal genes for hypertension, where the causal variants for gene expression and HTN were shared.

Extended Data Fig. 3 SNP effect on ACE and AGT.

a) The y-axis is the normalized expression of ACE in human kidney tubules (N = 356 samples), the x-axis is the PT cell fraction, each dot represents a single sample colored by their genotype C/C (red), C/T (green) and T/T (blue) at rs4292 locus. Two-sided P-value was calculated by eQTL(ci) model. b) eQTL meta-analysis (kidney and 46 GTEx tissues (v7)) showing the association between rs4292 and ACE. Each dot represents one tissue, and the size represents the M-value. Red dots: M-value ≥ 0.9, blue dots: M-value ≤ 0.1, green dots: 0.1 < M-value < 0.9. The y-axis shows the meta-analysis P of association in each single tissue. The x-axis shows the M-value; the posterior probability of the effect in each tissue estimated by METASOFT. c) The y-axis is the normalized expression of AGT in human kidney tubules, the x-axis is the PT cell fraction, each dot represents a single sample (N = 356) colored by their genotype C/C (red), C/T (green) and T/T (blue) at rs6687360 locus. Two-sided P-value was calculated by eQTL(ci) model. d) eQTL meta-analysis (kidney and 46 GTEx tissues (v7)) showing the association of rs6687360-AGT using eQTLs of kidney compartments and of 46 human tissues from GTEx (v7). Each dot represents one tissue, and the size represents the M-value. Red dots: M-value ≥ 0.9, blue dots: M-value ≤ 0.1, green dots: 0.1 < M-value < 0.9. The y-axis shows the meta-analysis P of association in each single tissue. The x-axis shows the M-value; the posterior probability of the effect in each tissue estimated by METASOFT.

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Supplementary Note, Figs. 1–36, Source Data for Supplementary Fig. 21 and Source Data for Supplementary Fig. 29.

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Source Data Fig. 2

Unprocessed scan of gel image for Fig. 2c.

Source Data Fig. 2

Relative transcript level (ABR).

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Sheng, X., Guan, Y., Ma, Z. et al. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat Genet 53, 1322–1333 (2021). https://doi.org/10.1038/s41588-021-00909-9

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