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.
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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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