Applying Polygenic Models to Disentangle Genotype-phenotype Associations across Common Human Diseases

In recent years, much human genetics research has focused on modeling the polygenic architecture of common human diseases using summary statistics from genome-wide association studies (GWAS). Utilizing longitudinal population-level data from participants in the UK Biobank (UKB) and the Canadian Partnership for Tomorrow’s Health (CanPath), I investigate how innovations in polygenic risk modelling may be applied to help disentangle genotype-phenotype associations for common human diseases represented across both these populations. By performing stratified GWAS, I capture the shared and distinct polygenic architecture of disease subgroups even among cases of a single ancestry. Subsequently, I compare the efficacy of polygenic risk modeling against more dynamic approaches such as transcriptional risk models which rely on gene expression profiles from tissue and may synergistically capture both individual-level genetic and environmental risk factors. Together, these findings present genetic risk factors for early-onset disease and provide insights into biomarkers for improving personalized disease risk prediction.M.Sc