Principal Investigator: Professor Paul Elliott
Department: Epidemiology and Biostatistics
Institution: Imperial College LondonTags: 236, blood pressure, genetics, GWAS, Hypertension
1a: High blood pressure (BP) is the leading risk factor for global disease burden. The role of several dietary/lifestyle risk factors in high BP is well-established. BP is a heritable trait; several genetic variants have reliably been associated with BP. The largest BP genome-wide association study (N=200,000) identified 29 genetic regions; some provided clues to BP physiology. UK Biobank provides a unique opportunity to a) identify novel genetic variants associated with BP in an unprecedented size (N=500,000); b) examine whether variants have effects on cardiovascular disease; c) test interactions between variants and other characteristics.
1b: This research proposal is consistent with UK Biobank’s mission of health related research in the public interest. We will examine robustly the relationship between genetic variants with blood pressure and cardiovascular disease and provide insights into biological mechanisms that might lead to high blood pressure. Our results will provide novel targets for treatment of high blood pressure and will inform risk assessment for the prevention of high blood pressure, potentially leading to more targeted, stratified approaches to treatments and “personalized healthcare” based on the specific needs of the individual.
1c: We will investigate cross-sectional associations between the genetic variants (genome wide association data) measured at baseline and blood pressure (BP) cross-sectionally. Findings will be deemed significance at the genome wide significance level (p<5×10-8). We will then test the association between genetic variants that are robustly associated with BP (based on our analysis and the literature) with incident myocardial infarction and stroke. Incident cases will be captured through ‘Spell and Episode” HES data. We will also seek potential interactions between pre-defined risk factors (age, obesity, diabetes, diet, physical activity) and genetic variants using various approaches.
1d: We include the full cohort in our analysis.