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

Xiaoguang Xu, James M. Eales, Artur Akbarov, Hui Guo, Lorenz Becker, David Talavera, Fehzan Ashraf, Jabran Nawaz, Sanjeev Pramanik, John Bowes, Xiao Jiang, John Dormer, Matthew Denniff, Andrzej Antczak, Monika Szulinska, Ingrid Wise, Priscilla R. Prestes, Maciej Glyda, Pawel Bogdanski, Ewa Zukowska-Szczechowska, Carlo Berzuini, Adrian S. Woolf, Nilesh J. Samani, Fadi J. Charchar and Maciej Tomaszewski ()
Additional contact information
Xiaoguang Xu: University of Manchester
James M. Eales: University of Manchester
Artur Akbarov: University of Manchester
Hui Guo: University of Manchester
Lorenz Becker: University of Manchester
David Talavera: University of Manchester
Fehzan Ashraf: University of Manchester
Jabran Nawaz: University of Manchester
Sanjeev Pramanik: University of Manchester
John Bowes: University of Manchester
Xiao Jiang: University of Manchester
John Dormer: University Hospitals of Leicester NHS Trust
Matthew Denniff: University of Leicester
Andrzej Antczak: Karol Marcinkowski University of Medical Sciences
Monika Szulinska: Karol Marcinkowski University of Medical Sciences
Ingrid Wise: Federation University Australia
Priscilla R. Prestes: Federation University Australia
Maciej Glyda: University of Zielona Góra
Pawel Bogdanski: Karol Marcinkowski University of Medical Sciences
Ewa Zukowska-Szczechowska: Silesian Medical College
Carlo Berzuini: University of Manchester
Adrian S. Woolf: Manchester University NHS Foundation Trust
Nilesh J. Samani: University of Leicester
Fadi J. Charchar: University of Leicester
Maciej Tomaszewski: University of Manchester

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-07260-4

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