Molecular insights into genome-wide association studies of chronic kidney disease-defining traits. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Xu, Xiaoguang; Eales, James; Akbarov, Artur; Guo, Hui; Becker, Lorenz; Talavera, David; Ashraf, Fehzan; Nawaz, Jabran; Pramanik, Sanjeev; Bowes, John; Jiang, Xiao; Dormer, John; Denniff, Matthew; Antczak, Andrzej; Szulinska, Monika; Wise, Ingrid; Prestes, Priscilla; Glyda, Maciej; Bogdanski, Pawel; Zukowska-Szczechowska, Ewa; Berzuini, Carlo; Woolf, Adrian; Samani, Nilesh; Charchar, Fadi; Tomaszewski, Maciej
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-07260-4
• Languages: English
• ISSNs: 2041-1723

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 andMUC1 ) on estimated glomerular filtration rate. We identify a common alternative splice variant inMUC1 (a gene responsible for rare Mendelian form of kidney disease) and observe increased renal expression of a specificMUC1 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. The molecular mechanisms that underlie associations in GWAS, incl. chronic kidney disease (CKD), are largely unknown. Here, the authors perform an integrative analysis of genetic, transcriptomic and epigenomic data from human kidney to pinpoint plausible molecular pathways of CKD genetic associations.