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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.

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.

References

  1. Jager, K. J. et al. A single number for advocacy and communication—worldwide more than 850 million individuals have kidney diseases. Kidney Int. 96, 1048–1050 (2019).

    Article  PubMed  Google Scholar 

  2. Alicic, R. Z., Rooney, M. T. & Tuttle, K. R. Diabetic kidney disease challenges, progress, and possibilities. Clin. J. Am. Soc. Nephrol. 12, 2032–2045 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Webster, A. C., Nagler, E. V., Morton, R. L. & Masson, P. Chronic kidney disease. Lancet 389, 1238–1252 (2017).

    Article  PubMed  Google Scholar 

  4. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Wuttke, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat. Genet. 51, 957–972 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ardlie, K. G. Human Genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

  8. Qiu, C. et al. Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. Nat. Med. 24, 1721–1731 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kim-Hellmuth, S. et al. Cell type-specific genetic regulation of gene expression across human tissues. Science 369, eaaz8528 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Giambartolomei, C. et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34, 2538–2545 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gusev, A. et al. A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants. Nat. Genet. 51, 815–823 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kato, M. & Natarajan, R. Diabetic nephropathy—emerging epigenetic mechanisms. Nat. Rev. Nephrol. 10, 517–530 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tonna, S., El-Osta, A., Cooper, M. E. & Tikellis, C. Metabolic memory and diabetic nephropathy: potential role for epigenetic mechanisms. Nat. Rev. Nephrol. 6, 332–341 (2010).

    Article  CAS  PubMed  Google Scholar 

  15. Sun, Y., Miao, N. & Sun, T. Detect accessible chromatin using ATAC-sequencing, from principle to applications. Hereditas 156, 29 (2019).

    Article  Google Scholar 

  16. Heintzman, N. D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311–318 (2007).

    Article  CAS  PubMed  Google Scholar 

  17. Muto, Y. et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat. Commun. 12, 2190 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wilson, P. C. et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc. Natl Acad. Sci. USA 116, 19619–19625 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Humphreys, B. D. & Knepper, M. A. Prioritizing functional goals as we rebuild the kidney. J. Am. Soc. Nephrol. 30, 2287–2288 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ransick, A. et al. Single-cell profiling reveals sex, lineage, and regional diversity in the mouse kidney. Dev. Cell 51, 399–413.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet. 50, 1140–1150 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kasela, S. et al. Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells. PLoS Genet. 13, e1006643 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Ishigaki, K. et al. Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat. Genet. 49, 1120–1125 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Jerber, J. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. 53, 304–312 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Huang, S., Sheng, X. & Susztak, K. The kidney transcriptome, from single cells to whole organs and back. Curr. Opin. Nephrol. Hypertens. 28, 219–226 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Aran, D., Hu, Z. & Butte, A. J. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dhillon, P. et al. The nuclear receptor ESRRA protects from kidney disease by coupling metabolism and differentiation. Cell Metab. 33, 379–394.e8 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Jew, B. et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat. Commun. 11, 1971 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Series B Stat. Methodol. 64, 479–498 (2002).

    Article  Google Scholar 

  35. Glastonbury, C. A., Couto Alves, A., El-Sayed Moustafa, J. S. & Small, K. S. Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs. Am. J. Hum. Genet. 104, 1013–1024 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Groopman, E. E. et al. Diagnostic utility of exome sequencing for kidney disease. N. Engl. J. Med. 380, 142–151 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Dennis, G. Jr. et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4, P3 (2003).

    Article  PubMed  Google Scholar 

  39. Aguet, F. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  40. Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).

    Article  CAS  PubMed  Google Scholar 

  41. Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017).

    PubMed  Google Scholar 

  43. Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Miao, Z. et al. Single cell resolution regulatory landscape of the mouse kidney highlights cellular differentiation programs and renal disease targets. Nat. Commun. 12, 2277 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Schmidt, E. M. et al. GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Hellwege, J. N. et al. Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program. Nat. Commun. 10, 3842 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Giri, A. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat. Genet. 51, 51–62 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Tin, A. et al. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels. Nat. Genet. 51, 1459–1474 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Teumer, A. et al. Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria. Nat. Commun. 10, 4130 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Pazoki, R. et al. GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits. Nat. Commun. 10, 3653 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Patsopoulos, N. A. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188(2019).

    Article  CAS  Google Scholar 

  56. Li, Y. et al. Integration of GWAS summary statistics and gene expression reveals target cell types underlying kidney function traits. J. Am. Soc. Nephrol. 31, 2326–2340 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Wang, S., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine-mapping. J. R. Stat. Soc. Series B Stat. Methodol. 82, 1273–1300 (2020).

    Article  Google Scholar 

  59. Ghandi, M. et al. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics 32, 2205–2207 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lee, D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics 32, 2196–2198 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Siva, N. 1000 Genomes project. Nat. Biotechnol. 26, 256 (2008).

    Article  PubMed  Google Scholar 

  62. Lin, L., Yee, S. W., Kim, R. B. & Giacomini, K. M. SLC transporters as therapeutic targets: emerging opportunities. Nat. Rev. Drug Discov. 14, 543–560 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mayer, G. J. et al. Analysis from the EMPA-REG OUTCOME trial indicates empagliflozin may assist in preventing the progression of chronic kidney disease in patients with type 2 diabetes irrespective of medications that alter intrarenal hemodynamics. Kidney Int. 96, 489–504 (2019).

    Article  CAS  PubMed  Google Scholar 

  64. Toto, R. D. Treatment of hypertension in chronic kidney disease. Semin. Nephrol. 25, 435–439 (2005).

    Article  PubMed  Google Scholar 

  65. Mokwe, E. et al. Determinants of blood pressure response to quinapril in black and white hypertensive patients: the Quinapril Titration Interval Management Evaluation trial. Hypertension 43, 1202–1207 (2004).

    Article  CAS  PubMed  Google Scholar 

  66. Kim, H. S. et al. Genetic control of blood pressure and the angiotensinogen locus. Proc. Natl Acad. Sci. USA 92, 2735–2739 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kobori, H., Harrison-Bernard, L. M. & Navar, L. G. Urinary excretion of angiotensinogen reflects intrarenal angiotensinogen production. Kidney Int. 61, 579–585 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

    Article  CAS  PubMed  Google Scholar 

  71. Donovan, M. K. R., D’Antonio-Chronowska, A., D’Antonio, M. & Frazer, K. A. Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants. Nat. Commun. 11, 4426 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Halekoh, U. & Højsgaard, S. J. Stat. Softw. https://doi.org/10.18637/jss.v059.i09 (2014).

  73. Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1–15 (2021).

    Article  CAS  Google Scholar 

  74. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).

    Article  CAS  PubMed  Google Scholar 

  76. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Leland, M., John, H., Nathaniel, S. & Lukas, G. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

    Article  Google Scholar 

  79. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  81. Skene, N. G. & Grant, S. G. N. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Front. Neurosci. 10, 16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Luo, Y. et al. Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations. Hum. Mol. Genet. https://doi.org/10.1093/hmg/ddab130 (2021).

  84. Brown, M. B. 400: a method for combining non-independent, one-sided tests of significance. Biometrics 31, 987–992 (1975).

    Article  Google Scholar 

  85. Morris, A. P. et al. Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies. Nat. Commun. 10, 29 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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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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