학술논문
Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease
Document Type
article
Author
Sandholm, Niina; Cole, Joanne B; Nair, Viji; Sheng, Xin; Liu, Hongbo; Ahlqvist, Emma; van Zuydam, Natalie; Dahlström, Emma H; Fermin, Damian; Smyth, Laura J; Salem, Rany M; Forsblom, Carol; Valo, Erkka; Harjutsalo, Valma; Brennan, Eoin P; McKay, Gareth J; Andrews, Darrell; Doyle, Ross; Looker, Helen C; Nelson, Robert G; Palmer, Colin; McKnight, Amy Jayne; Godson, Catherine; Maxwell, Alexander P; Groop, Leif; McCarthy, Mark I; Kretzler, Matthias; Susztak, Katalin; Hirschhorn, Joel N; Florez, Jose C; Groop, Per-Henrik
Source
Diabetologia. 65(9)
Subject
Language
Abstract
Aims/hypothesisDiabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.MethodsWe performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.ResultsThe meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p