A genome-wide association study identifies a possible role for cannabinoid signalling in the pathogenesis of diabetic kidney disease

Abstract

Diabetic kidney disease (DKD), also known as diabetic nephropathy, is the leading cause of renal impairment and end-stage renal disease. Patients with diabetes are at risk for DKD because of poor control of their blood glucose, as well as nonmodifiable risk factors including age, ethnicity, and genetics. This genome-wide association study (GWAS) was conducted for the first time in the Emirati population to investigate possible genetic factors associated with the development and progression of DKD. We included data on 7,921,925 single nucleotide polymorphism (SNPs) in 258 cases of type 2 diabetes mellitus (T2DM) who developed DKD and 938 control subjects with T2DM who did not develop DKD. GWAS suggestive results (P < 1 × 10^-5) were further replicated using summary statistics from three cohorts with T2DM-induced DKD (Bio Bank Japan data, UK Biobank, and FinnGen Project data) and T1DM-induced DKD (UK-ROI cohort data from Belfast, UK). When conducting a multiple linear regression model for gene-set analyses, the CNR2 gene demonstrated genome-wide significance at 1.46 × 10^-6. SNPs in CNR2 gene, encodes cannabinoid receptor 2 or CB2, were replicated in Japanese samples with the leading SNP rs2501391 showing a P_combined = 9.3 × 10^-7, and odds ratio = 0.67 in association with DKD associated with T2DM, but not with T1DM, without any significant association with T2DM itself. The allele frequencies of our cohort and those of the replication cohorts were in most cases markedly different. In addition, we replicated the association between rs1564939 in the GLRA3 gene and DKD in T2DM (P = 0.016, odds ratio = 0.54 per allele C). Our findings suggest evidence that cannabinoid signalling may be involved in the development of DKD through CB2, which is expressed in different kidney regions and known to be involved in insulin resistance, inflammation, and the development of kidney fibrosis.

Figure 1

Q-Q plots and Manhattan plots of the DKD GWAS and gene-based test using FUMA and MAGMA. (A) Quantile–Quantile plot of the GWAS data. The observed p-values, on a log-10 scale, are plotted against their expected values under the null hypothesis assuming none of the sites have an effect. (B) Quantile–Quantile plot of the gene-based data. The observed p-values, on a log-10 scale, are plotted against their expected values under the null hypothesis assuming none of the sites have an effect. (C) Manhattan plot of the DKD GWAS. The plot shows the p-values on a log-10 scale (y-axis) by their chromosomal location (x-axis). (D) Manhattan plot of the DKD gene-based test as computed by MAGMA. The plot shows the p-values on a log-10 scale (y-axis) by their chromosomal location (x-axis). Input SNPs were mapped to 18,124 protein coding genes. Genome wide significance (Red dashed line in the plot) was defined at p = 0.05/18,124 = 2.759 × 10^–6, labelling one gene (CNR2) that reached genome wide significance.