UCLA

Population precision health represents a paradigm shift in healthcare, emphasizing theneed for tailored and personalized approaches to improve health outcomes at a population level. Population precision health recognizes the heterogeneity within populations and leverages advances in genomics, epigenomics, and clinical data repositories to deliver targeted interventions and preventive strategies. By integrating genomic and clinical data, population precision health aims to identify individuals at increased risk for specific diseases and tailor interventions based on their unique genetic and environmental profiles. In this work, I present strategies to address three key challenges of implementing population precision health. I develop algorithms to non-invasively detect tissue death, which can be used for disease diagnosis and prevention. I then use these algorithms as the foundation of a scalable cell-free DNA platform to monitor disease at the population level. Lastly, I employ machine learning algorithms in a large genetic biobank to identify population-specific genetic and health risks. Together, this work represents a step toward implementing non-invasive disease screening and monitoring in diverse groups, which will be a crucial element of deploying population precision health.