An eQTL landscape of kidney tissue in human nephrotic syndrome
Christopher E Gillies,
Nephrotic Syndrome Study Network (NEPTUNE),
Matthew G. Sampson
Posted 14 Mar 2018
bioRxiv DOI: 10.1101/281162 (published DOI: 10.1016/j.ajhg.2018.07.004)
Posted 14 Mar 2018
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 eQTLs studies of human kidney. Here, we used whole genome sequencing (WGS) and microdissected glomerular (GLOM) & tubulointerstitial (TI) transcriptomes from 187 patients 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) transcriptomes. 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 cell-type specificity of significant eQTLs. We integrated eQTL data with a published IgA Nephropathy (IGAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1767 TI eQTLs at FDR <0.05. 14% and 19% of GLOM & TI eQTLs, respectively, had > 1 independent signal associated with its expression. 12.2% and 26.3% 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 & TI genes, primarily at the HLA region. In this study of NS patients, we discovered GLOM & TI eQTLs, identified those that were tissue-specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. This data is publicly available for browsing and download; http://nephqtl.org.
- Downloaded 1,187 times
- Download rankings, all-time:
- Site-wide: 9,044 out of 92,758
- In genomics: 1,331 out of 5,851
- Year to date:
- Site-wide: 39,501 out of 92,758
- Since beginning of last month:
- Site-wide: 50,966 out of 92,758
Downloads over time
Distribution of downloads per paper, site-wide
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!