Utilizing data from Finland, where genetic details of approximately 500,000 individuals are linked to a national health registry, this presentation will explore the use of polygenic scores and electronic health records (EHRs) to predict disease susceptibility and progression. It will highlight the challenges and successes of employing polygenic scores in clinical trial design, emphasizing their potential to enhance trial efficiency and cost-effectiveness. Additionally, the talk will discuss the application of machine learning algorithms to predict healthcare outcomes from EHRs, assessing their fairness and generalizability across different populations. The presentation will conclude with a direct comparison of genetic and EHR-based predictions to assess which approach is most predictive and generalizable across healthcare systems.

Associate Professor at FIMM and HiLIFE and a research associate at Harvard Medical School and Massachusetts General Hospital. Previously he did his post-doc at the Analytical and Translation Genetic Unit at Massachusetts General Hospital/Harvard Medical School/Broad Institute and his PhD at Karolinska Institute. His research interests lie at the intersection between epidemiology, genetics and statistics. His research vision is to integrate genetic data and information from electronic health record/national health registries to enhance early detection of common diseases and enable more effective public health interventions.