Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

Niina Sandholm, Joanne B. Cole, Viji Nair, Xin Sheng, Hongbo Liu, Emma Ahlqvist, Natalie van Zuydam, Emma H. Dahlström, Damian Fermin, Laura J Smyth, Rany M Salem, Carol Forsblom, Erkka Valo, Valma Harjutsalo, Eoin P. Brennan, Gareth J. McKay, Darrell Andrews, Ross Doyle, Helen C. Looker, Robert G. NelsonColin Palmer, Amy Jayne McKnight, Catherine Godson, Alexander P. Maxwell, Leif Groop, Mark I. McCarthy, Matthias Kretzler, Katalin Susztak, Joel N. Hirschhorn (Lead / Corresponding author), Jose C. Florez, Per-Henrik Groop (Lead / Corresponding author), GENIE Consortium

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Abstract

Aims/hypothesis: Diabetic 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.

Methods: We 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.

Results: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m 2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10 −9; although not withstanding correction for multiple testing, p>9.3×10 −9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10 −6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10 −6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10 −11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10 −8] and negatively with tubulointerstitial fibrosis [p=2.0×10 −9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10 −16], and SNX30 expression correlated positively with eGFR [p=5.8×10 −14] and negatively with fibrosis [p<2.0×10 −16]).

Conclusions/interpretation: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). Graphical abstract: [Figure not available: see fulltext.].

Original languageEnglish
Pages (from-to)1495-1509
Number of pages15
JournalDiabetologia
Volume65
Early online date28 Jun 2022
DOIs
Publication statusPublished - Sep 2022

Keywords

  • Diabetes complications
  • Diabetic kidney disease
  • Genetics
  • Genome-wide association study; Meta-analysis; Transcriptomics

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