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.