Dissecting the pleiotropic effects of established type 2 diabetes and other cardiometabolic trait loci to define pathways and gene networks involved in type 2 diabetes pathogenesis

Background and aims: Recent genome-wide association studies (GWAS) for human complex phenotypes have identified hundreds of genetic variants for cardio-metabolic traits and risk of disease. At many loci or specific variants associations are observed with multiple epidemiologically correlated traits. We formed the Cross-Consortia Pleiotropy Group to investigate the patterns of multi-cardio-metabolic trait associations across the genome. We aimed (a) to examine the associations of cardio-metabolic trait loci with epidemiologically correlated traits by grouping shared patterns of individual trait effects; (b) to define pathway and gene networks involved in the trait variability within the association pattern groups. Materials and methods: We evaluated the genetic effects of 544 independent variants (r2<0.8) from a total of 687 SNPs from published GWAS meta-analyses (thru Sep 2012) of 20 quantitative cardio-metabolic traits, including systolic/diastolic blood pressure, 8 glycaemic, 6 obesity/anthropometric, 4 lipid traits, and 2 diseases (Type 2 Diabetes (T2D), hypertension). We applied a complete hierarchical cluster analysis, which grouped variants according to their impact on the cardio-metabolic traits. We combined these data with annotated pathways, protein-protein interactions and semantic relationships from the published literature using GRAIL and DAPPLE software tools, which estimated the significance of connections between putative genes.