Assessing risk of cardiometabolic disease is central to early detection, prevention and clinical decision-making. Until now, clinical risk prediction relies on demographic characteristics (age, gender, ethnicity), lifestyle (smoking, alcohol consumption, diet, physical exercise), clinical parameters (body mass index, blood pressure, blood chemistries) and family history. Evidently, routine genetic testing is absent from this list. Significantly, genetics are the earliest measurable contributor to common heritable adult-onset disease risk. Thus, novel genetic profiling methods have been developed to estimate the probabilistic susceptibility of an individual to disease based on their Polygenic Risk Score (PRS).
Research question: Investigating the degree of diverse risk alleles of SNPs affecting the onset of cardiovascular diseases and type 2 diabetes and how they interplay with non-genetic factors, such as smoking status, Body Mass Index (BMI), serum total cholesterol, blood glucose (fasting), blood pressure, physical activity, and diet.
Aim: To produce a PRS based on effect size estimates derived from GWAS summary statistic data (e.g., effect sizes or P values) of SNPs for six cardiovascular diseases (CVDs), comprising Coronary Artery Disease, Hypertrophic Cardiomyopathy, Dilated Cardiomyopathy, Heart failure, Atrial Fibrillation, and Ischemic Stroke as also Type 2 Diabetes. In addition, to produce an adjusted PRS, as an integrated tool, through the incorporation of non-genetic factors such as smoking status, Body Mass Index (BMI), serum total cholesterol, blood glucose (fasting), blood pressure, physical activity, and diet. Finally, to validate the predictive power of the developed Polygenic Risk Score (PRS) for the abovementioned diseases using genetic and phenotypic data from the UK Biobank.
