The advent of clinical genome sequencing to identify patients at risk for serious diseases and to tailor treatments promises to greatly improve health outcomes and provide a foundation for the delivery of Precision Medicine. However, even as laboratory methods to perform sequencing become highly efficient, uncertainty persists around the optimal breadth and economic value of sequencing as well as which individuals should be tested. As we rapidly approach an era of inexpensive sequencing, new approaches to quantify and optimize the economic and clinical value of genome-tailored care are needed. As part of the Rational Integration of Sequencing (RISE) project funded by NHGRI between 2016-2021, we developed decision analytic models of three Center for Disease Control (CDC) Tier 1 conditions: hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia. The models estimate the average clinical efficacy and cost-effectiveness of population genomic screening. Currently, the Network is developing a similar multiplexed approach to assess the impact of polygenic risk scores as part of the Rational Integration of Polygenic Risk Scores (RIPS) project.