Data from: Molecular insights into genome-wide association studies of chronic kidney disease-defining traits

Xu, Xiaoguang, University of Manchester

Eales, James M., University of Manchester

Akbarov, Artur, University of Manchester

Guo, Hui, University of Manchester

Becker, Lorenz, University of Manchester

Talavera, David, University of Manchester

Ashraf, Fezhan, University of Manchester

Nawaz, Jabran, University of Manchester

Pramanik, Sanjeev, University of Manchester

Bowes, John, University of Manchester

Jiang, Xiao, University of Manchester

Dormer, John, University Hospitals of Leicester NHS Trust

Denniff, Matthew, University of Leicester

Antczak, Andrzej, Department of Urology and Uro-oncology, Karol Marcinkowski University of Medical Sciences, Poznan, Poland

Szulinska, Monika, Department of Internal Medicine, Metabolic Disorders and Hypertension, Karol Marcinkowski University of Medical Sciences, Poznan, Poland

Wise, Ingrid, Federation University

Prestes, Priscilla R., Federation University

Glyda, Maciej, University of Zielona Góra

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

Zukowska-Szczechowska, Ewa, Department of Health Care, Silesian Medical College, Katowice, Poland

Berzuini, Carlo, University of Manchester

Woolf, Adrian S., Manchester University NHS Foundation Trust

Samani, Nilesh J., University of Leicester

Charchar, Fadi J., University of Leicester

Tomaszewski, Maciej, University of Manchester

Published Dec 10, 2018 on Dryad. https://doi.org/10.5061/dryad.10r1pt0

Cite this dataset

Xu, Xiaoguang et al. (2018). Data from: Molecular insights into genome-wide association studies of chronic kidney disease-defining traits [Dataset]. Dryad. https://doi.org/10.5061/dryad.10r1pt0

Abstract

Genome-wide association studies (GWAS) have identified >100 loci of chronic kidney disease-defining traits (CKD-dt). Molecular mechanisms underlying these associations remain elusive. Using 280 kidney transcriptomes and 9958 gene expression profiles from 44 non-renal tissues we uncover gene expression partners (eGenes) for 88.9% of CKD-dt GWAS loci. Through epigenomic chromatin segmentation analysis and variant effect prediction we annotate functional consequences to 74% of these loci. Our colocalisation analysis and Mendelian randomisation in >130,000 subjects demonstrate causal effects of three eGenes (NAT8B, CASP9 and MUC1) on estimated glomerular filtration rate. We identify a common alternative splice variant in MUC1 (a gene responsible for rare Mendelian form of kidney disease) and observe increased renal expression of a specific MUC1 mRNA isoform as a plausible molecular mechanism of the GWAS association signal. These data highlight the variants and genes underpinning the associations uncovered in GWAS of CKD-dt.

Usage notes

Gene expression data

Normalised gene expression values from RNA sequencing. Gene-level TPM values from Kallisto were log-transformed, normalised by robust quantile normalisation, outliers replaced with imputed values and transformed using the rank-based inverse normal transformation method. Please see methods of linked publication for further details.

TRANSLATE-genes.txt