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

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=9.8x10(-9); although not withstanding correction for multiple testing, p>9.3x10(-9)). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p<2.7x10(-6)). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1x10(-6)). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5x10(-11)). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6x10(-8)] and negatively with tubulointerstitial fibrosis [p=2.0x10(-9)], tubular DCLK1 expression correlated positively with fibrosis [p=7.4x10(-16)], and SNX30 expression correlated positively with eGFR [p=5.8x10(-14)] and negatively with fibrosis [p<2.0x10(-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).

Automated summary

By performing meta-analysis of previous genome-wide association studies (GWAS) on diabetic kidney disease (DKD) and integrating the results with renal transcriptomics datasets, novel genetic factors and genes contributing to DKD have been identified.