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

BackgroundDiabetes is the leading cause of kidney disease, and heritability studies demonstrate a substantial, yet poorly understood, contribution of genetics to kidney complications in people with diabetes. MethodsWe performed genome-wide association study (GWAS) meta-analyses using ten different phenotypic definitions of diabetic kidney disease (DKD), including nearly 27,000 individuals with diabetes, and integrated the results with various kidney omics datasets. ResultsThe meta-analysis identified a novel low frequency intronic variant (rs72831309) in the TENM2 gene encoding teneurin transmembrane protein 2 associated with a lower risk of the combined chronic kidney disease (CKD; eGFR<60 ml/min/1.73 m2) and DKD (microalbuminuria or worse) phenotype ("CKD-DKD", odds ratio 2.08, p=9.8x10-9). 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 data with human glomerular and tubular expression data in a transcriptome-wide association study demonstrated higher tubular AKIRIN2 gene expression in DKD versus non-DKD controls (p=1.1x10-6). The lead SNPs within the DCLK1, AKIRIN2, SNX30 and three other gene regions significantly alterated the methylation at this region in kidneys (p<2.2x10-11). Expression of target genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes. For example, tubular TENM2 expression positively correlated with eGFR (p=2.3x10-9) and negatively with tubulointerstitial fibrosis (p=4.7x10-9), tubular DCLK1 expression positively correlated with fibrosis (p=1.6x10-12), and SNX30 level positively correlated with eGFR (p=7.6x10-13) and negatively with fibrosis (p<2x10-16). ConclusionsGWAS meta-analysis and integration with renal omics data points to novel genes contributing to pathogenesis of DKD.