One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics.

ABSTRACT: Ensemble learning has been increasingly popular for boosting the predictive power of polygenic risk scores (PRS), with almost every recent multi-ancestry PRS approach employing ensemble learning as a final step. Existing ensemble approaches rely on individual-level data for model training, which severely limits their real-world applications, especially in non-European populations without sufficient genomic samples. Here, we introduce a statistical framework to construct regularized ensemble PRS, which allows us to combine a large number of candidate PRS models using only summary statistics from genome-wide association studies. We demonstrate its robust and substantial improvement over many existing PRS models in both within- and cross-ancestry applications. We believe this is truly "one sc

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC11642782 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature