Title: Systems genomics for population health: From risk prediction to aetiology

Brief abstract: Over the past 15 years, the generation of high-dimensional omics data in population research has become common place. In this talk, I’ll cover challenges and recent advances in using genomics, metabolomics, proteomics and other modalities for the purposes of disease risk prediction and understanding aetiological factors. These will include the development, application and translation of polygenic scores with a focus on cardiovascular diseases; genetic imputation of proteomes, metabolomes and transcriptomes; the design and deployment of open computational tools and resources, including the Polygenic Score Catalog and OmicsPred; and the use of genetics to understand the role of the microbiome in human traits.

Biography: Mike grew up in the Seattle area before beginning undergraduate study in 1999 at the University of Washington, where he graduated with BSc’s in biochemistry and economics. As a 19 year-old, Mike began analyzing data from the draft Human Genome Project, spending several years doing research in gene finding and protein structure prediction. He continued studying protein structure as a graduate student at UCLA, but returned to genomics in 2005 when he moved to the Wellcome Trust Sanger Institute (Cambridge, UK). While at Sanger, Mike completed his PhD with Prof Leena Peltonen and Prof Gert-Jan van Ommen and was heavily involved in the analytics for the first wave of genome-wide association studies as well as large-scale studies integrating multi-omic data. After a postdoc at the Walter and Eliza Hall Institute (Melbourne, AU), he was recruited to the faculty at the University of Melbourne in 2012 where he built a research program in systems genomics with a focus on clinical and public health applications. In 2017, Mike was recruited to the Baker Institute and the University of Cambridge to set up a lab spanning Australia and the UK that focuses on core areas of systems genomics, including polygenic risk scores, integrated analysis of multi-omics data and development of analytic tools.