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Am J Hum Genet. 2018 Aug 2;103(2):232-244. doi: 10.1016/j.ajhg.2018.07.004. Epub 2018 Jul 26.

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome.

Author information

1
Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA.
2
Department of Computational Medicine and Bioinformatics, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA.
3
Department of Medicine-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA.
4
Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA 02114, USA; Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA.
5
Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA.
6
Department of Medicine, Division of Nephrology, College of Physicians & Surgeons, Columbia University, New York, NY, USA.
7
Department of Computational Medicine and Bioinformatics, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA; Department of Medicine-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA.
8
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
9
Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA. Electronic address: mgsamps@med.umich.edu.

Abstract

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."

KEYWORDS:

eQTL; expression quantitative trait loci; focal segmental glomerulosclerosis; genomics; glomerulus; kidney; minimal change disease; nephrotic syndrome; podocyte; proteinuria; single-cell RNA sequencing

PMID:
30057032
PMCID:
PMC6081280
[Available on 2019-02-02]
DOI:
10.1016/j.ajhg.2018.07.004

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