Developing multiancestry polygenic risk scores

Music City Center 

The usual practice for building and applying polygenic risk scores is to first divide populations being studied into ancestrally homogenous subsets, performing genome wide association studies on the subsets and developing polygenic risk scores for each subset. When the scores are applied to assess risk for disease some sort of correction is needed to address heterogeneity in the target population. This approach is disadvantageous because it cannot include individuals of mixed ancestry in the initial risk score modeling, ignores the variability in linkage disequilbrium among populations which can refine the identification of causal variants and cannot be applied directly to individuals of mixed ancestry or who do not align with the assumed homogenous sets. In this presentation, we provide an alternate approach based on optimal application of mixed models that can include related individuals and individuals of mixed ancestry in the polygenic risk model development. We subsequently evaluate polygenic risk score application to populations who are enrolled in clinical trials to assess the impact of precision behavioral medicine in change smoking behavior and adoption of lung cancer screening.