Polygenic risk scores for prediction of cause-specific disease and mortality risk in Mexicans
Polygenic risk scores (PRSs) may enhance risk stratification and prevention for chronic diseases and associated mortality. Studies from populations of European ancestry have demonstrated that genetic instruments can improve the prediction of cardiovascular and other chronic disease risk when added to conventional risk factors. Consequently, they may be more informative for treatment and prevention strategies (such as strategies related to the use of blood pressure, lipid-lowering or reno-protective drugs) than scores derived only from conventional risk factors. However, the value of such scores in non-European populations is uncertain.
This project will explore these questions using data from the Mexico City Prospective Study (MCPS), a cohort study of 150,000 adults followed for two decades with detailed baseline information on socio-demographic factors, lifestyle characteristics, and physical and biological measurements (including NMR metabolomics, genetic array and exome data in the whole cohort).
The specific aims of this project will be subject to student interest and discussion with the supervisors but could involve:
- developing and evaluating genetic-risk instruments for specific major diseases of interest.
- estimating differences in risk-stratification by different genetic and conventional risk scores, and the potential implications for disease-specific prevention and treatment in a general population setting.
- testing the transferability of the constructed disease-specific PRS to other contemporaneous populations (eg, using the UK Biobank data).
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The student will work in a multi-disciplinary team and will gain experience in non-communicable diseases research, genetic epidemiology and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, study design for causal inference in a general population context, statistical programming and data analysis, including GWAS and PhWAS analyses. The student will be supported to publish their results and present at conferences.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
Training in advanced statistics, epidemiological methods, programming, and scientific writing will be available as required. Attendance at seminars, workshops and courses provided by the Department and University will be encouraged.
The ideal candidate will have a Master's degree in a relevant area (e.g. genetic epidemiology, biomedical or life sciences, statistics) and proficiency with programing analyses in R, STATA, Python, or SAS packages.