Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Abstract

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10^-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.

Figure 1.. Heatmap of associations of 37 cardiometabolic phenotypes with eight mechanistic clusters of index SNVs for T2D association signals.

Each column corresponds to a cluster. Each row corresponds to a cardiometabolic phenotype. The “temperature” of each cell represents the Z-score (aligned to the T2D risk allele) of association of the phenotype with index SNVs assigned to the cluster. *Phenotype is adjusted for body mass index.

Figure 2.. Heatmap of cluster-specific enrichments of T2D associations for cell type-specific regions of open chromatin derived from single-cell ATAC-seq peaks in adult and foetal tissue.

Each column represents a mechanistic cluster. Each row represents a cell type that was significantly enriched (P<0.00023, Bonferroni correction for 222 cell types) for T2D associations in at least one cluster (indicated by an asterisk). The temperature of each cell defines the magnitude of the log-fold enrichment. The liver/lipid metabolism cluster is not presented because it includes only three T2D association signals and the regression model did not converge.

Figure 3.. Associations of the overall GRS and cluster-specific partitioned GRS with five T2D-related vascular outcomes in up to 137,559 individuals from multiple ancestry groups.

Each of the panels summarise the associations of the overall GRS and each cluster-specific component of the partitioned GRS with coronary artery disease (CAD), peripheral artery disease (PAD), ischemic stroke (IS), end-stage diabetic nephropathy (ESDN), and proliferative diabetic retinopathy (PDR). The height of each bar corresponds to the log-odds ratio (beta) per standard deviation of the GRS, and the grey bar shows the 95% confidence interval. Analyses of T2D-related macrovascular complications (CAD, PAD, and IS) were undertaken in all individuals, with adjustment for T2D status. Analysis of microvascular complications were undertaken in individuals with T2D only. *P<0.05, nominal association. **P<0.0063, Bonferroni correction for eight clusters.