Genetic and environmental factors for premature mortality in contrasting populations

BACKGROUND

In recent decades, the prevalence of obesity and diabetes has increased in many countries and the worldwide mortality rates attributed to obesity have doubled. Genetic risks and hazardous environments increase the risk for many chronic diseases and premature deaths. However, both the patterns for unhealthy lifestyles and the genetic variation that determines major risk factors and associated mortality differ between populations or by admixed ancestries. 

The aim of the project is to investigate commonalities and differences in genetic predisposition and lifestyle habits that explain risk for premature mortality in contrasting populations. The Mexico City Prospective Study (MCPS) and the UK Biobank (UKB) provide unique platforms for addressing these questions. Together, these studies include more than 0.6 million adults with genotype, lifestyle, metabolomics and medical information. Inclusion of data from the China Kadoorie Biobank (CKB) might be also considered. 

The specific aims of the project could include: 

1. characterising potential differences in genetic predisposition to major causes of premature mortality by lifestyle habits in the study populations. This could include use of polygenic risk scores to predict specific diseases and major preventable risk factors (e.g. adiposity, lipids, HbA1c, blood-pressure), and key lifestyle determinants for health (e.g. smoking, exercise, drinking, dietary patterns)
2. assessing differences in risk between vascular-metabolic versus other causes of premature mortality
3. assessing heterogeneity in gene-environment mortality risk across different ancestries (e.g., European, Indigenous American, Asia, other).

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience and skills in genetic epidemiology research in a general population context, analysis of large-scale prospective data using specific software, statistical programming, bioinformatics data analysis, and presentation skills. The student will be supported to publish peer-reviewed papers emerging from their DPhil. 

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, and participation in in-house workshops and lectures will be expected.

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 in R, Python, SUGEN, packages.