Chronic kidney disease (CKD), a condition in which the kidneys are unable to clear waste products, affects 700 million people globally. Genome-wide association studies (GWASs) have identified sequence variants for CKD; however, the biological basis of these GWAS results remains poorly understood. To address this issue, we created an expression quantitative trait loci (eQTL) atlas for the glomerular and tubular compartments of the human kidney. Through integrating the CKD GWAS with eQTL, single-cell RNA sequencing and regulatory region maps, we identified novel genes for CKD. Putative causal genes were enriched for proximal tubule expression and endolysosomal function, where DAB2, an adaptor protein in the TGF-β pathway, formed a central node. Functional experiments confirmed that reducing Dab2 expression in renal tubules protected mice from CKD. In conclusion, compartment-specific eQTL analysis is an important avenue for the identification of novel genes and cellular pathways involved in CKD development and thus potential new opportunities for its treatment.
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The eQTL data is publicly available at http://susztaklab.com/eqtl. RNA-seq data have been deposited in the Gene Expression Omnibus (GEO) with the accession code GSE115098. As the samples were collected from de-identified kidney tissue samples, no consent was obtained to share individual-level genotype data.
Eckardt, K. U. et al. Evolving importance of kidney disease: from subspecialty to global health burden. Lancet 382, 158–169 (2013).
Webster, A. C., Nagler, E. V., Morton, R. L. & Masson, P. Chronic kidney disease. Lancet 389, 1238–1252 (2017).
Rhee, C. M. & Kovesdy, C. P. Epidemiology: spotlight on CKD deaths—increasing mortality worldwide. Nat. Rev. Nephrol. 11, 199–200 (2015).
Sud, M., Tangri, N., Pintilie, M., Levey, A. S. & Naimark, D. M. Progression to Stage 4 chronic kidney disease and death, acute kidney injury and hospitalization risk: a retrospective cohort study. Nephrol. Dial. Transplant. 31, 1122–1130 (2016).
Garrett, M. R., Pezzolesi, M. G. & Korstanje, R. Integrating human and rodent data to identify the genetic factors involved in chronic kidney disease. J. Am. Soc. Nephrol. 21, 398–405 (2010).
Braun, D. A. et al. Mutations in nuclear pore genes NUP93, NUP205 and XPO5 cause steroid-resistant nephrotic syndrome. Nat. Genet. 48, 457–465 (2016).
Tory, K. et al. Mutation-dependent recessive inheritance of NPHS2-associated steroid-resistant nephrotic syndrome. Nat. Genet. 46, 299–304 (2014).
Fox, C. S. et al. Genomewide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: the Framingham Heart Study. J. Am. Soc. Nephrol. 15, 2457–2461 (2004).
Pattaro, C. et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat. Commun. 7, 10023 (2016).
Köttgen, A. et al. New loci associated with kidney function and chronic kidney disease. Nat. Genet. 42, 376–384 (2010).
Okada, Y. et al. Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat. Genet. 44, 904–909 (2012).
Köttgen, A. et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nat. Genet. 41, 712–717 (2009).
Witte, J. S., Visscher, P. M. & Wray, N. R. The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765–776 (2014).
Chatterjee, N., Shi, J. & García-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).
Pasaniuc, B. & Price, A. L. Dissecting the genetics of complex traits using summary association statistics. Nat. Rev. Genet. 18, 117–127 (2017).
Korstanje, R. & Paigen, B. From QTL to gene: the harvest begins. Nat. Genet. 31, 235–236 (2002).
Pashos, E. E. et al. Large, diverse population cohorts of hipscs and derived hepatocyte-like cells reveal functional genetic variation at blood lipid-associated loci. Cell Stem Cell 20, 558–570 (2017).
Gupta, R. M. et al. A genetic variant associated with five vascular diseases is a distal regulator of endothelin-1 gene expression. Cell 170, 522–533 (2017).
Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).
Capellini, T. D. et al. Ancient selection for derived alleles at a GDF5 enhancer influencing human growth and osteoarthritis risk. Nat. Genet. 49, 1202–1210 (2017).
Soldner, F. et al. Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression. Nature 533, 95–99 (2016).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).
The ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).
Roadmap Epigenomic Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
GTEx Consortium. The Genotype–Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
GTEx Consortium. Human genomics. The Genotype–Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Ko, Y. A. et al. Genetic-variation-driven gene-expression changes highlight genes with important functions for kidney disease. Am. J. Hum. Genet. 100, 940–953 (2017).
Yeo, N. C. et al. Shroom3 contributes to the maintenance of the glomerular filtration barrier integrity. Genome Res. 25, 57–65 (2015).
Trudu, M. et al. Common noncoding UMOD gene variants induce salt-sensitive hypertension and kidney damage by increasing uromodulin expression. Nat. Med. 19, 1655–1660 (2013).
Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).
Schiffer, M. et al. Pharmacological targeting of actin-dependent dynamin oligomerization ameliorates chronic kidney disease in diverse animal models. Nat. Med. 21, 601–609 (2015).
Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).
Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).
Fairfax, B. P. et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).
Ishigaki, K. et al. Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat. Genet. 49, 1120–1125 (2017).
Lee, J. W., Chou, C. L. & Knepper, M. A. Deep sequencing in microdissected renal tubules identifies nephron segment–specific transcriptomes. J. Am. Soc. Nephrol. 26, 2669–2677 (2015).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45 D1, D896–D901 (2017).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
Nica, A. C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).
Howard, M. F. et al. Mutations in PGAP3 impair GPI-anchor maturation, causing a subtype of hyperphosphatasia with mental retardation. Am. J. Hum. Genet. 94, 278–287 (2014).
Araki, T., Hayashi, M., Nakanishi, K., Morishima, N. & Saruta, T. Caspase-9 takes part in programmed cell death in developing mouse kidney. Nephron Exp. Nephrol. 93, e117–e124 (2003).
Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).
Gillies, C. E. et al. An eqtl landscape of kidney tissue in human nephrotic syndrome. Am. J. Hum. Genet. 103, 232–244 (2018).
Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 88, 586–598 (2011).
Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).
Meng, X. M., Nikolic-Paterson, D. J. & Lan, H. Y. TGF-β: the master regulator of fibrosis. Nat. Rev. Nephrol. 12, 325–338 (2016).
Borges, F. T. et al. TGF-β1-containing exosomes from injured epithelial cells activate fibroblasts to initiate tissue regenerative responses and fibrosis. J. Am. Soc. Nephrol. 24, 385–392 (2013).
Sato, M., Muragaki, Y., Saika, S., Roberts, A. B. & Ooshima, A. Targeted disruption of TGF-β1/Smad3 signaling protects against renal tubulointerstitial fibrosis induced by unilateral ureteral obstruction. J. Clin. Invest. 112, 1486–1494 (2003).
Hocevar, B. A., Smine, A., Xu, X. X. & Howe, P. H. The adaptor molecule Disabled-2 links the transforming growth factor beta receptors to the Smad pathway. EMBO J. 20, 2789–2801 (2001).
Prunier, C. & Howe, P. H. Disabled-2 (Dab2) is required for transforming growth factor beta-induced epithelial to mesenchymal transition (EMT). J. Biol. Chem. 280, 17540–17548 (2005).
Edeling, M., Ragi, G., Huang, S., Pavenstädt, H. & Susztak, K. Developmental signalling pathways in renal fibrosis: the roles of Notch, Wnt and Hedgehog. Nat. Rev. Nephrol. 12, 426–439 (2016).
Reidy, K., Kang, H. M., Hostetter, T. & Susztak, K. Molecular mechanisms of diabetic kidney disease. J. Clin. Invest. 124, 2333–2340 (2014).
Zhuo, J. L. & Li, X. C. Proximal nephron. Compr. Physiol. 3, 1079–1123 (2013).
Kimura, T. et al. Autophagy protects kidney proximal tubule epithelial cells from mitochondrial metabolic stress. Autophagy 9, 1876–1886 (2013).
Dickson, L. E., Wagner, M. C., Sandoval, R. M. & Molitoris, B. A. The proximal tubule and albuminuria: really! J. Am. Soc. Nephrol. 25, 443–453 (2014).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Delaneau, O., Marchini, J. & Zagury, J. F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).
Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).
Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).
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).
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).
Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Series B Stat. Methodol. 64, 479–498 (2002).
Sul, J. H., Han, B., Ye, C., Choi, T. & Eskin, E. Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches. PLoS Genet. 9, e1003491 (2013).
Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Tao, W., Moore, R., Smith, E. R. & Xu, X. X. Endocytosis and physiology: insights from disabled-2 deficient mice. Front. Cell Dev. Biol. 4, 129 (2016).
Moore, R., Cai, K. Q., Tao, W., Smith, E. R. & Xu, X. X. Differential requirement for Dab2 in the development of embryonic and extra-embryonic tissues. BMC Dev. Biol. 13, 39 (2013).
Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
The authors thank the Molecular Pathology and Imaging Core (P30-DK050306) and Diabetes Research Center (P30-DK19525) at the University of Pennsylvania for their services. This work in the Susztak lab has been supported by the National Institute of Health NIH R01 DK087635, DK076077 and DP3108220, Boehringer Ingelheim, the Eli Lilly Co. and the Juvenile Diabetes Research Foundation.
J. Hill, P.J., J. Hawkins, C.M.B.-K. and S.S.P. are full-time employees of Boehringer Ingelheim Pharmaceuticals, Inc. This work has been supported by Boehringer Ingelheim Pharmaceuticals, Inc. and the Eli Lilly Co.
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Qiu, C., Huang, S., Park, J. et al. Renal compartment–specific genetic variation analyses identify new pathways in chronic kidney disease. Nat Med 24, 1721–1731 (2018). https://doi.org/10.1038/s41591-018-0194-4
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