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

Background Diabetes 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. Methods We 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. Results The 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 m 2 ) and DKD (microalbuminuria or worse) phenotype (“CKD-DKD”, odds ratio 2.08, p =9.8×10 −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 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 =1.1×10 −6 ). The lead SNPs within the DCLK1, AKIRIN2, SNX30 and three other gene regions significantly alterated the methylation at this region in kidneys ( p <2.2×10 −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 =2.3×10 −9 ) and negatively with tubulointerstitial fibrosis ( p =4.7×10 −9 ), tubular DCLK1 expression positively correlated with fibrosis ( p =1.6×10 −12 ), and SNX30 level positively correlated with eGFR ( p =7.6×10 −13 ) and negatively with fibrosis ( p <2×10 −16 ). Conclusions GWAS meta-analysis and integration with renal omics data points to novel genes contributing to pathogenesis of DKD.