PROJECT SUMMARYCirculating levels of lipids glucose and insulin result from complex and interwoven physiological mechanismsare indicators of type-2 diabetes (T2D) and cardiovascular disease (CVD) risk and may also be implicated inthe greater risk of T2D among statin users. Although high triglyceride levels are considered a risk factor for thedevelopment of T2D a recent and novel finding emerging from our group suggests that genetic susceptibility toelevated triglyceride levels is protective of T2D. This counter-intuitive finding and similar findings by othergroups suggests that the link between lipid and glycemic traits is complex. Here we propose that genomic andstatistical approaches that leverage the context-dependency of genetic effects can afford us greater power toidentify novel loci acting at the interface of lipid and glycemic traits improve our understanding of currentlyknown loci and provide insight into the specific mechanisms underlying this interface and cardiometabolicdisease in general. We thus hypothesize that there are additional loci beyond the univariate GWAS-identifiedones which are associated with both lipid and glycemic traits in similar and/or opposite directions. Secondlywe hypothesize that the association between genetic factors and lipid levels differs according to an individual'slevel of insulin resistance and that the association between genetic factors and glycemic levels differsaccording to an individual's level of dyslipidemia. Thirdly we hypothesize that there are genetic loci that couldpredict the extent to which someone taking lipid-lowering medications such as statins is likely to progress toT2D. Finally we will follow-up our findings with further refined phenotypes and examine associatedmethylation and gene expression patterns and putative regulatory function. To test these hypotheses we willuse multiple large genomic datasets obtained through the Database of Genotypes and Phenotypes (dbGaP)and from the UK Biobank. The latter dataset notably comprises longitudinal and detailed phenotypic dataalong with genome-wide data on 500000 individuals - an unprecedented resource in genomic research. Theuse of prospective cohort studies from these sources will allow us to leverage 1) multiple existing sets of broadand detailed phenotypic data including lipoprotein sub-fraction measurements and 2) the availability oflongitudinal data on glycemic and lipid measurements along with information on medication use. It isanticipated that this project will 1) contribute to our understanding of the biological links between glycemic andlipid traits which are major early indicators of cardiometabolic disease 2) identify genes that may be up- ordown-regulated in the context of insulin resistance and dyslipidemia 3) identify genetic variants that may affectglycemic response to cholesterol-lowering drugs and 4) provide insight into the underlying biologicalmechanisms. Our findings may lead to more fine-tuned and early assessments of T2D and CVD risk and thedevelopment of targeted prevention and treatment strategies.