Medical School
Twin Cities
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.
These researchers combine theoretical modeling and analysis of empirical data to study these relationships with the long-term goal to improve understanding of the genetic basis of disease risk in diverse populations. They are using MSI's computational capabilities for simulation of large-scale (~100,000 genomes) datasets under complex evolutionary histories, and analyzing publicly available large-scale genomic and phenotypic data (e.g. UK Biobank).