Arslan Zaidi

Genome-wide association studies (GWAS) have been extremely successful at discovering genetic variants underlying many diseases. There is a great deal of interest in translating these findings to estimate an individual’s genetic risk for disease (so-called polygenic risk) to be used in the clinic for early prevention and intervention. Unfortunately, the predictive ability of polygenic risk prediction is at present limited and varies as a function of ancestry. This is because both variant discovery and prediction accuracy depend on the genetic variation underlying complex traits, which in turn is shaped by population history. We combine theoretical modeling and analysis of empirical data to study these relationships with the long-term goal to improve our understanding of the genetic basis of disease risk in diverse populations.