Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities
Loesch DP., Garg M., Matelska D., Vitsios D., Jiang X., Ritchie SC., Sun BB., Runz H., Whelan CD., Holman RR., Mentz RJ., Moura FA., Wiviott SD., Sabatine MS., Udler MS., Gause-Nilsson IA., Petrovski S., Oscarsson J., Nag A., Paul DS., Inouye M.
Abstract Genomics can provide insight into the etiology of type 2 diabetes and its comorbidities, but assigning functionality to non-coding variants remains challenging. Polygenic scores, which aggregate variant effects, can uncover mechanisms when paired with molecular data. Here, we test polygenic scores for type 2 diabetes and cardiometabolic comorbidities for associations with 2,922 circulating proteins in the UK Biobank. The genome-wide type 2 diabetes polygenic score associates with 617 proteins, of which 75% also associate with another cardiometabolic score. Partitioned type 2 diabetes scores, which capture distinct disease biology, associate with 342 proteins (20% unique). In this work, we identify key pathways (e.g., complement cascade), potential therapeutic targets (e.g., FAM3D in type 2 diabetes), and biomarkers of diabetic comorbidities (e.g., EFEMP1 and IGFBP2) through causal inference, pathway enrichment, and Cox regression of clinical trial outcomes. Our results are available via an interactive portal (https://public.cgr.astrazeneca.com/t2d-pgs/v1/).