PROJECT SUMMARY The key appeal of polygenic risk scores (PRS) is the provision of individual-level estimates of genetic liability to complex disease. These proxies of genetic liability enable a raft of applications across basic research and clinical settings. However, while PRS are set to play a pivotal role in the future of biomedical research, their present formulation is suboptimal for application across diverse and admixed populations. To address this we propose to develop high-resolution modeling to optimize the computation of PRSs across diverse populations, which will: (i) use Bayesian hierarchical modeling to account for the population genetic and statistical causes of low PRS portability between populations, (ii) deconstruct genetic risk into shared, ancestry-specific and gene*environment sub-components, (iii) produce pathway-based PRSs that can help expose the functional sources of the portability problem and explain ancestry disease heterogeneity. The key deliverable will be the production of a suite of powerful PRS tools tailored to diverse and admixed populations. The rationale is that failure to model important structural features that are inherent to diverse clinical populations constitutes a vital loss of information. By modeling this high-resolution data in statistically principled and rigorous ways, researchers will be equipped to perform powerful PRS prediction across all human populations and in all individuals. This will offer unprecedented predictive power and insights into disease mechanisms. In Aim 1, we develop a Bayesian hierarchical PRS method, BridgePRS3, that models differences in LD, effect sizes and allele frequencies between ancestries and their constituent sub-ancestries. In Aim 2, we build a novel method, admixPRS, for application to admixed individuals that deconstructs the genome into local ancestry tracts corresponding to sub-ancestries, accounting for known admixture history, and decomposing genetic risk into 3 sub-components. In Aim 3, we develop a pathway-based PRS method for diverse populations, PRSet+. Finally, we will build a unifying PRS method, globalPRS, that calculates PRS in any individual, of any ancestry. Our proposal is significant because the burgeoning application of PRS means that reducing disparity in PRS predictive power will have immediate, high impact in diverse populations. By performing high-resolution modeling to boost PRS predictive power by mirroring the structure of human populations, and exposing gene*environment and pathway-level contributions to the PRS portability problem, our suite of PRS tools have the potential to increase the clinical utility of PRS and our understanding of how genetic risk varies in global populations. Our proposal is innovative because we develop the first Bayesian hierarchical PRS tools to model the high- resolution structure of diverse and admixed populations, in relation to: (i) ancestry (modeling sub-ancestries), genetic risk (3-component admixPRS model...