A GWAS atlas of adiposity traits in Mexicans
Obesity and diabetes are major causes of premature disease and mortality, are increasing worldwide, and are particularly common in Latin-America. Abdominal and gluteofemoral fat depots have opposing relationships with the risk of cardiometabolic diseases for reasons that are not well understood. Genetics may help unravel these relationships but there is very limited population-based genetic-evidence in non-European populations.
The Mexico City Prospective Study (MCPS) includes 150,000 adults followed for two decades with detailed baseline information on socio-demographic factors, lifestyle characteristics, and physical and biological measurements (including NMR metabolomic, genetic array and exome data in the whole cohort). It provides a unique platform for genetic discovery studies of adipose traits in a Hispanic population with a high prevalence of obesity and diabetes.
The specific aims of this project will be subject to student interest and discussion with the supervisors but could involve:
- characterising the genetic architecture of adiposity traits (e.g. BMI, fat and lean mass, WHR, hip and waist circumferences) in an admixed Mexican cohort through genome-wide association analyses. This would include resolving conditionally-independent associations, identifying novel variants that may be segregating at higher frequencies in Indigenous American populations, and genetic fine-mapping
- leveraging local ancestry analysis to detect population-specific genetic associations and systemically evaluate heterogeneity in genetic effects across ancestries for various adiposity traits (for example, using the TRACTOR method)
- incorporating tissue- and cell-specific epigenomic annotations to resolve the molecular basis of genetic associations with adiposity through genome-wide enrichment analyses and molecular QTL colocalisation analyses at trait-associated loci.
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
Training in advanced genetic epidemiology, statistics, programming, and scientific writing will be provided. Attendance at seminars, workshops and courses provided by the department and University will also be encouraged. There will be opportunity to present research work at relevant international/national conferences.
The ideal candidate will have a Master's degree in a relevant area (e.g. genetic epidemiology/biomedical or life sciences) and proficiency with programing analyses in R, Python, SUGEN, DEPICT packages.