Project Summary There has been extraordinary growth in new techniques to predict common, complex disease based on polygenic risk scores (PRS). Without an understanding grounded in evidence, it is unlikely that the clinical use of PRS will propagate from highly specialized applications and environments to become adopted more broadly and provide greater benefit to the US population. Critical challenges include: 1) understanding the impact of clinical PRS for multiple diseases on long-term patient outcomes, 2) identifying risk thresholds for return of results that optimize patient outcomes and provide cost-effective care, 3) understanding how PRS performance across diverse populations may affect existing disparities and subsequent patient outcomes. We propose to address these challenges using decision analytic modeling and by building on our extensive work in this area to create a novel framework capable of assessing PRSs in the context of monogenic and clinical risks. We have already created clinical-economic models to project lifetime clinical impact and cost-effectiveness for population-level genomic screening with return of monogenic disease risks associated with three CDC Tier 1 conditions: hereditary breast and ovarian cancer, Lynch syndrome, and familial hyperlipidemia. As part of this proposal, titled Rational Integration of Polygenic Risk Scores (RIPS), we will create models to assess the clinical outcomes and economic value of population screening using PRS in real- world settings and applied to large and diverse populations. The Aims of the proposal include 1) to evaluate published and real-world evidence on the clinical value of adding PRS to inform comprehensive genomic risk assessment; 2) to understand the impact of PRS performance and return risk thresholds on incremental clinical benefit and cost effectiveness for breast cancer, atherosclerotic cardiovascular disease, and colorectal cancer, and 3) to develop research priorities for the equitable development and implementation of PRS across underserved and underrepresented populations.