1. Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease
- Author
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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, and Groop, Per-Henrik
- Subjects
Human Genome ,Kidney Disease ,Prevention ,Genetics ,Diabetes ,Biotechnology ,2.1 Biological and endogenous factors ,Aetiology ,Renal and urogenital ,Metabolic and endocrine ,Good Health and Well Being ,Diabetes Mellitus ,Type 2 ,Diabetic Nephropathies ,Doublecortin-Like Kinases ,Fibrosis ,Genome-Wide Association Study ,Humans ,Intracellular Signaling Peptides and Proteins ,Kidney ,Polymorphism ,Single Nucleotide ,Protein Serine-Threonine Kinases ,Diabetes complications ,Diabetic kidney disease ,Genome-wide association study ,Meta-analysis ,Transcriptomics ,GENIE Consortium ,Genome-wide association study ,Meta-analysis ,Transcriptomics ,Clinical Sciences ,Paediatrics and Reproductive Medicine ,Public Health and Health Services ,Endocrinology & Metabolism - 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
- Published
- 2022