Background Two decades of genome-wide association studies (GWAS) have led to fast-growing application of polygenic risk prediction (PRS). However, due to population structure and evolutionary path difference, the PRS substrate derived mostly on studies of European ancestry does not work equally well for other ancestries. There is an association between prediction accuracy decay and individual genetic distance (GD) to the genetic centers (GC) of various populations. Objectives To develop a new PRS method and software that utilizes individual GD so as to improve PRS risk prediction accuracy, especially for non-European populations. Method We hypothesize that adding a GD-based weight into PRS methods would enhance its risk prediction performance especially for minority groups. We explore the GD first by principal components (PC) and then by phylogenetic tree structures. Building on top of an emerging software (PRS-CSx) that achieves high prediction accuracy across multiple-ancestries, we present PGS-GRID, where "GRID" stands for "Genetic Reference based on Individual Distance". Results We developed a preliminary version of PRS-GRID and pilot tested its prediction performance for a classic quantitative trait (e.g., height) and a disease trait (e.g., type-2 diabetes (T2DM)). We found slight but noticeable improvement of risk prediction for minority populations. We further explored SHapley Additive exPlanation (SHAP) so that the performance of PRS-GRID could be clearly explained and visualized, which is a key step for PRS to be used in clinical and public health practice. Conclusions The PRS-GRID philosophy and method represent an innovative and significant advancement in the field of polygenic risk prediction. Our work provides a foundation for future research and clinical applications aimed at reducing health disparities and improving population health through personalized medicine.

The authors have declared no competing interest.

This study did not receive any funding

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