The Genetic Landscape of Renal Complications in Type 1 Diabetes

Abstract

Diabetes is the leading cause of ESRD. Despite evidence for a substantial heritability of diabetic kidney disease, efforts to identify genetic susceptibility variants have had limited success. We extended previous efforts in three dimensions, examining a more comprehensive set of genetic variants in larger numbers of subjects with type 1 diabetes characterized for a wider range of cross-sectional diabetic kidney disease phenotypes. In 2843 subjects, we estimated that the heritability of diabetic kidney disease was 35% (P=6.4×10^-3). Genome-wide association analysis and replication in 12,540 individuals identified no single variants reaching stringent levels of significance and, despite excellent power, provided little independent confirmation of previously published associated variants. Whole-exome sequencing in 997 subjects failed to identify any large-effect coding alleles of lower frequency influencing the risk of diabetic kidney disease. However, sets of alleles increasing body mass index (P=2.2×10^-5) and the risk of type 2 diabetes (P=6.1×10^-4) associated with the risk of diabetic kidney disease. We also found genome-wide genetic correlation between diabetic kidney disease and failure at smoking cessation (P=1.1×10^-4). Pathway analysis implicated ascorbate and aldarate metabolism (P=9.0×10^-6), and pentose and glucuronate interconversions (P=3.0×10^-6) in pathogenesis of diabetic kidney disease. These data provide further evidence for the role of genetic factors influencing diabetic kidney disease in those with type 1 diabetes and highlight some key pathways that may be responsible. Altogether these results reveal important biology behind the major cause of kidney disease.

Keywords: diabetic kidney disease; genetics and development; genome-wide association study; whole exome sequencing.

Figure 1.

Schematic picture of the DKD phenotypic comparisons on the basis of measured AER and eGFR, encompassing different stages and severities of DKD. ‘Combined DKD’ and ‘Late DKD’ (conventionally used in many previous genetic studies of DKD) are expected to capture genetic factors affecting DKD in general; ‘Early DKD’ comparison targets the genetic factors affecting the initiation of DKD, or with milder effect on the phenotype, whereas the two ESRD-based case definitions are expected to capture factors related to the late progression of DKD such as fibrotic processes, or genetic factors with particularly strong effect on the phenotype. Although the ‘CKD’ phenotype may reveal genetic factors for reduced renal function irrespective of the presence of albuminuria, the ‘CKD+DKD’ phenotype is an extreme phenotype that requires that controls have no signs of renal complications (neither AER or eGFR based). AER thresholds are given in µg/min, eGFR thresholds in ml/min per 1.73 m². Number of samples per subphenotype: Normo: 2593; miA: 800; maA: 944; ESRD: 813; no CKD: 2909; CKD: 1255; no CKD, no DKD: 2018; CKD+DKD: 1117. Normo, normal AER; miA, microalbuminuria; maA, macroalbuminuria.

Figure 2.

The proportion of phenotypic variance explained by genotypes (V[G]/V[p_L]), i.e., the narrow-sense heritability, of the seven studied DKD phenotypic comparisons indicate high heritability especially for the more extreme phenotype definitions. Error bars represent the SEM.

Figure 3.

The GRS for BMI and T2D were associated with DKD phenotypes in subjects with T1D. A GRS for MBI (z-transformed) was associated (P<2.6×10⁻³ to account for multiple testing; 19 GRS traits) with combined and late DKD, and CKD phenotypes; GRS for T2D was associated with late DKD.

Figure 4.

Genome-wide comparison of DKD traits and other phenotypes, evaluated with LD score regression, shows negative correlation between DKD traits and smoking cessation. Significant correlations (P<0.003 to account for multiple testing; 17 related phenotypes) are indicated with *. Bars represent 95% confidence intervals.