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Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney

Nature Genetics volume 53, pages 630–637 (2021)Cite this article

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Abstract

The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

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Fig. 1: cis-eQTL analysis of the human kidney and genetic variants identified in GWASs of BP (BP-GWAS SNPs).
Fig. 2: cis-sQTL and cis-mQTL analysis of the human kidney and variants identified in GWASs of BP (BP-GWAS SNPs).
Fig. 3: Colocalization and MR analyses.

Data availability

The data supporting the results presented in this article are either available in the Supplementary Information (Supplementary Tables and Supplementary Note) or can be obtained from the authors upon reasonable request. The normalized gene expression, splice junction usage and DNA methylation data are archived at the Dryad digital repository (https://doi.org/10.5061/dryad.15dv41nvx). The e-, s- and mQTL summary statistics are available in the Supplementary Tables.

Code availability

Our studies make use of well-established computational and statistical analysis software and these are fully referenced in the Methods. All custom code used to orchestrate these analyses is available on request.

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Acknowledgements

This work was supported by British Heart Foundation project grants no. PG/17/35/33001 and no. PG/19/16/34270 and Kidney Research UK grants no. RP_017_20180302 and no. RP_013_20190305 to M.T., National Institutes of Health (NIH) grant no. R01 DK117445-01A1 to A.P.M., NIH (USA) NIDDK grants no. R01 DK108805 and no. R01 DK119380 to M.G.S., British Heart Foundation Personal Chair CH/13/2/30154 and Manchester Academic Health Science Centre: Tissue Bank Grant to B.K., and Medical University of Silesia grants no. KNW-1-152/N/7/K to J.Z. and no. KNW-1-171/N/6/K to W.W. T.J.G. acknowledges support from the European Research Council (ERC-CoG-Inflammatension grant no. 726318) and the European Commission/Narodowe Centrum Badan i Rozwoju, Poland (EraNet-CVD-Plaquefight). P.M. acknowledges support of British Heart Foundation grant no. PG/19/84/34771. D.T. acknowledges support of Medical Research Council New Investigator Research Grant no. MR/R010900/1. B.K. is supported by a British Heart Foundation Personal Chair. G.T. is supported by the Wellcome Trust (grant no. WT206194) and Open Targets. E.C.-G. is supported by a Gates Cambridge Scholarship (no. OPP1144). The Nephrotic Syndrome Study Network Consortium (NEPTUNE), U54-DK-083912, is a part of the NIH Rare Disease Clinical Research Network (RDCRN), supported through a collaboration between the NIH Office of Rare Diseases Research, the National Center for Advancing Translational Sciences and the National Institute of Diabetes, Digestive, and Kidney Diseases. Additional funding and/or programmatic support for this project has also been provided by the University of Michigan, NephCure Kidney International and the Halpin Foundation. Access to TCGA kidney and GTEx data has been granted by the NIH (approvals 50804-2 and 50805-2). The results published here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. We thank the Oxford Genomics Centre at the Wellcome Centre for Human Genetics funded by the Wellcome Trust (grant reference no. 203141/Z/16/Z) for the generation and initial processing of sequencing data.

Author information

Author notes

  1. Sanjeev Pramanik

    Present address: East Lancashire Hospitals NHS Trust, Blackburn, UK

  2. Mikael Ekholm

    Present address: Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden

  3. These authors contributed equally: James M. Eales, Xiao Jiang, Xiaoguang Xu.

Authors and Affiliations

  1. Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK

    James M. Eales, Xiao Jiang, Xiaoguang Xu, Sushant Saluja, Artur Akbarov, Sanjeev Pramanik, Mahan Salehi, Yusif Shakanti, Mikael Ekholm, Alicja Nazgiewicz, David Talavera, Bernard Keavney & Maciej Tomaszewski

  2. Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK

    Eddie Cano-Gamez & Gosia Trynka

  3. Division of Nephrology, Boston Children’s Hospital, Boston, MA, USA

    Michelle T. McNulty & Matthew G. Sampson

  4. The Broad Institute, Cambridge, MA, USA

    Michelle T. McNulty & Matthew G. Sampson

  5. Institute of Cardiovascular Science, University College London, London, UK

    Christopher Finan, Sandesh Chopade & Aroon Hingorani

  6. Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK

    Hui Guo & Carlo Berzuini

  7. Department of General, Vascular and Transplant Surgery, Medical University of Silesia, Katowice, Poland

    Wojciech Wystrychowski & Robert Król

  8. Department of Obesity, Metabolic Disorders Treatment and Clinical Dietetics, Karol Marcinkowski University of Medical Sciences, Poznan, Poland

    Monika Szulinska & Pawel Bogdanski

  9. Division of Evolution and Genomic Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK

    Huw B. Thomas & Raymond T. O’Keefe

  10. Health Innovation and Transformation Centre, School of Science, Psychology and Sport, Federation University Australia, Ballarat, Victoria, Australia

    Priscilla R. Prestes & Fadi J. Charchar

  11. Australian Institute of Tropical Health & Medicine, James Cook University, Cairns, Queensland, Australia

    Ingrid Wise

  12. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    Evangelos Evangelou

  13. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece

    Evangelos Evangelou

  14. Department of Cardiovascular Sciences, University of Leicester, Leicester, UK

    Matthew Denniff, Nilesh J. Samani & Fadi J. Charchar

  15. Department of Medicine, University of Michigan, Ann Arbor, MI, USA

    Felix Eichinger & Matthias Kretzler

  16. Department of Urology and Uro-oncology, Karol Marcinkowski University of Medical Sciences, Poznan, Poland

    Bradley Godfrey & Andrzej Antczak

  17. Department of Transplantology and General Surgery Poznan, Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland

    Maciej Glyda

  18. Division of Musculoskeletal and Dermatological Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK

    Stephen Eyre, John Bowes & Andrew P. Morris

  19. Division of Research and Innovation, Manchester University NHS Foundation Trust, Manchester, UK

    Jason Brown

  20. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    Mark Caulfield

  21. National Institute for Health Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK

    Mark Caulfield

  22. Department of Health Care, Silesian Medical College, Katowice, Poland

    Ewa Zukowska-Szczechowska

  23. Department of Internal Medicine, Diabetology and Nephrology, Zabrze, Medical University of Silesia, Katowice, Poland

    Joanna Zywiec

  24. Division of Cell Matrix Biology and Regenerative Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK

    Adrian S. Woolf

  25. Royal Manchester Children’s Hospital and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK

    Adrian S. Woolf

  26. Division of Cardiology and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK

    Bernard Keavney

  27. Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

    Pasquale Maffia

  28. Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

    Pasquale Maffia & Tomasz J. Guzik

  29. Department of Pharmacy, University of Naples Federico II, Naples, Italy

    Pasquale Maffia

  30. Department of Internal and Agricultural Medicine, Jagiellonian University College of Medicine, Kraków, Poland

    Tomasz J. Guzik

  31. Open Targets, Wellcome Genome Campus, Cambridge, UK

    Gosia Trynka

  32. National Institute for Health Research, Leicester Biomedical Research Centre, Leicester, UK

    Nilesh J. Samani

  33. Harvard Medical School, Boston, MA, USA

    Matthew G. Sampson

  34. Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Liverpool, UK

    Andrew P. Morris

  35. Department of Physiology, University of Melbourne, Parkville, Victoria, Australia

    Fadi J. Charchar

  36. Manchester Heart Centre and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK

    Maciej Tomaszewski

Authors
  1. James M. Eales
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  2. Xiao Jiang
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Contributions

J.M.E., X.J., X.X., S.S. and A. Akbarov performed the main analytical and experimental tasks. E.C.-G., M.T.M., C.F., H.G., H.B.T., S.P., S.C., P.R.P., I.W., E.E., M. Salehi, Y.S., M.E., M.D., F.E., B.G., S.E., C.B., J. Bowes, M.C., M.K., A.S.W., D.T., B.K., P.M., T.J.G., R.T.-O’K., G.T., N.J.S., A.H., M.G.S., A.P.M. and F.J.C. provided additional analyses and data. W.W., M. Szulinska, A. Antczak, M.G., R.K., J. Brown, E.Z.-S., J.Z. and P.B. were involved in the collection of kidney resources. M.D., A.N., P.R.P., I.W. and F.J.C. were involved in additional sample processing and sequencing. J.M.E., X.J., X.X., F.J.C., A.P.M. and M.T. contributed to drafting the manuscript. M.T. performed the overall supervision of the project. All authors reviewed and approved the accepted version of the manuscript.

Corresponding author

Correspondence to Maciej Tomaszewski.

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Competing interests

M.K. reports grants from the NIH and Goldfinch Bio during the conduct of the study, and grants from AstraZeneca, Gilead, Novo Nordisk, Eli Lilly, Janssen, Merck, Elipidera, Certa and Boehringer Ingelheim, outside the submitted work.

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Extended data

Extended Data Fig. 1 rs4750358 as BP-GWAS kidney sSNP and its BEND7 (BEN Domain Containing 7) target.

a, The BP-elevating (A) allele of rs4750358 creates a branch point (orange box) for a new downstream acceptor splice site and facilitates excision of isoform I7b. The branch point for intron excision isoform (IEI) I7a is also marked (yellow box). b, Association between rs4750358 and kidney expression of I7a IEI of BEND7, I7b IEI of BEND7 and total renal expression of BEND7 (Wald test, linear regression). Data are standardized expression values stratified on rs4750358 genotype. All sample sizes (n) are biologically independent samples. Outliers (outside 1.5x interquartile range) are denoted as points. Whiskers denote extent of 1.5x interquartile range. Upper, middle and lower box lines denote 75th, 50th and 25th percentiles, respectively. FDR, false discovery rate; NS, non-significant. c, Simplified intron-exon structures of the terminal portions of renal BEND7-002 isoforms. d, Proportional expression of BEND7 mRNA isoforms in the kidney. e, Amino acids and phosphorylation sites in the C-terminus of protein isoforms BEND7-002a and BEND7-002b. Exon 8b in BEND7-002b protein isoform contains a longer coding sequence replacing a pair of valines with a 20-amino acid stretch (with many polar, mostly charged, amino acids and three additional possible phosphorylation sites at the C-terminus). The orange bar above the peptide sequence shows the amino acids encoded by the last exon. Polar amino acids are highlighted in red, whereas hydrophobic amino acids are shown in blue. Asterisks are used to identify possible phosphorylation sites, for each of which a list of possible protein kinases is provided.

Extended Data Fig. 2 rs4932373 as a BP-GWAS kidney mSNP.

a, rs4932373 (orange solid bar) is a BP-GWAS kidney mSNP. Its renal target (cg04510874, blue solid bar) maps onto the CpG island (green segment) and the promoter region of the FES gene. Chromatin states are shown at the bottom (green, Transcribed; yellow, Enhancer; red, TSS). Yellow solid bar, sentinel BP-GWAS SNP (rs2521501); black dotted bar, single nucleotide variants; red solid bar, CpG sites. b, The opposite direction of effect of rs4932373 genotype on renal expression of FES and kidney DNA methylation at cg04510874 (Wald test, linear regression). Outliers (outside 1.5x interquartile range) are denoted as points. Whiskers denote extent of 1.5x interquartile range. Upper, middle and lower box lines denote 75th, 50th and 25th percentiles, respectively. All sample sizes (n) are biologically independent samples. P, level of statistical significance.

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Eales, J.M., Jiang, X., Xu, X. et al. Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney. Nat Genet 53, 630–637 (2021). https://doi.org/10.1038/s41588-021-00835-w

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