Genome-Wide Association Study of Quantitative Kidney Function in 52,531 Individuals with Diabetes Identifies Five Diabetes-Specific Loci

Key Points Comprehensive genome-wide association study of eGFR in diabetes, accounting for diabetes duration, kidney disease, and known modifiers, identified novel genetic effects. Incorporation of various kidney multi-omics data provides supporting evidence for the role of novel genome-wide association study loci in diabetic kidney disease. Background Diabetic kidney disease (DKD) is a serious diabetes complication caused by both environmental and genetic risk factors. Previous genome-wide association studies (GWAS) have identified several loci associated with kidney function and kidney disease in the general population and, to a lesser extent, in diabetes. Methods To uncover the genetic factors driving diabetes-induced kidney function, we conducted a series of GWAS meta-analyses of eGFR in 17,267 individuals with type 1 diabetes and 35,264 with type 2 diabetes (52,531 total), using multiple well-characterized cohorts of type 1 diabetes DKD and data from the UK Biobank and SUrrogate markers for Micro- and Macrovascular hard end points for Innovative diabetes Tools (SUMMIT) consortium. We further accounted for DKD case/control status, diabetes duration and subtype, body mass index, glycated hemoglobin levels, and the relationship between eGFR and albuminuria. Results GWAS identified 13 loci associated with eGFR ( P < 5×10 −8 ), with five loci (candidate genes: HIPK3 , TRIM5 , RORA , ERBB4 , and BCL6 / LPP ) not associated with or were in opposite directions as compared with eGFR in the general population. Four candidate genes ( HIPK3 , BCL6, LPP , and RORA ) demonstrated evidence of differential expression in kidney compartments and cells among subgroups with DKD or diabetes versus controls. Lead single-nucleotide polymorphisms rs8027829 ( RORA ) and rs76300256 ( BCL6/LPP ) were methylation quantitative trait loci in whole blood and kidney tissue, respectively, and rs76300256 and its related CpGs all cluster in a kidney enhancer. Conclusions Our integrated approach identified candidate genes with diabetes-specific effects on kidney function.