Polygenic prediction for underrepresented populations through transfer learning by utilizing genetic similarity shared with European populations.

ABSTRACT: Because current genome-wide association studies are primarily conducted in individuals of European ancestry and information disparities exist among different populations, the polygenic score derived from Europeans thus exhibits poor transferability. Borrowing the idea of transfer learning, which enables the utilization of knowledge acquired from auxiliary samples to enhance learning capability in target samples, we propose transPGS, a novel polygenic score method, for genetic prediction in underrepresented populations by leveraging genetic similarity shared between the European and non-European populations while explaining the trans-ethnic difference in linkage disequilibrium (LD) and effect sizes. We demonstrate the usefulness and robustness of transPGS in elevated prediction accuracy via

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC11794457 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature