"Using Finnish big data to understand the genetic and epidemiology of common diseases"
Dr. Andrea Ganna
Andrea Ganna and his research group are interested in finding new ways to early identify common preventable diseases. To do that they develop statistical and deep learning approaches and apply them to millions of health information from electronic health record/national health registries. They then integrate registry-based information with genetic information from large biobank-based studies to help identify groups of individuals that can most benefit from existing pharmacological interventions. Finally, they aim to implement these approaches in the clinic and evaluate their cost-effectiveness. They are also interested in using trans-national Scandinavian registries to ask basic questions about human nature/nurture and evolution. For example, they are interested in understanding which disease are currently under strongest selection and if they can see the impact of selection within large-scale genetic data.
Andrea is an EMBL-group leader at FIMM and an instructor 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 on the intersection between epidemiology, genetics and statistics. Andrea has authored and co-authored both methodological and applied papers focused on leveraging large scale epidemiological datasets to identify novel socio-demographic, metabolic and genetic markers of common complex diseases.
He has extensive expertise in statistical genetics and has been working with large-scale exome and genome sequencing data, focusing on ultra-rare variants in coding and non-coding regions.
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 public health interventions.