Polygenic risk scores for prediction of specific diseases and mortality risk in Mexicans

BACKGROUND

Polygenic risk scores (PRSs) can potentially 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 classification of chronic disease risk, when added to conventional risk factors. In addition, if applied at a population level, genetic risk scores can be informative for treatment and prevention strategies over and above conventional risks alone. However, the value of such scores for predicting risk from different diseases associated with major mortality in non-European populations, such as Mexicans, is uncertain. Furthermore, the clinical implications of using polygenic information for initiating preventive interventions (e.g. vasculo-protective or reno-protective, respiratory therapy), are largely unknown in Mexicans.

These questions can be addressed in the Mexico City Prospective Study (MCPS), a prospective cohort study of 150,000 participants with socio-demographic and lifestyle characteristics, medication and disease history, biological characteristics (including genetics and NMR-metabolomics) and 20 years of follow-up for cause-specific mortality. The MCPS provides an opportunity to assess the relevance of traditional and genetic risk scores to premature mortality from renal failure, heart failure, COPD, cerebrovascular, neoplastic, hepatic or infective causes, using conventional and genetic approaches.

The specific aims will be tailored to the applicant’s interests but could include:

• developing and evaluating genetic risk instruments for a particular disease of interest (above)
• estimating differences in risk-stratification by different genetic and conventional risk scores, including potential implications for disease-specific prevention and treatment
• testing the transferability of the constructed disease-specific PRS to other contemporaneous populations from the UK Biobank and the China Kadoorie Biobank.

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 prospective data. They will develop skills in study design for causal inference in a general population context, statistical programming and data analysis. The student will be supported to publish their results and present at conferences.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project will be based within the MCPS group at the Big Data Institute, a world-class community for population health research. In-house training in statistical and epidemiological methods, programming, and scientific writing will be provided. 

PROSPECTIVE  STUDENT

The ideal candidate will have a good first degree and MSc in statistics, epidemiology, genetics, biomedical sciences or a related subject, and proficiency with programing analyses using various software.