Optimizing and benchmarking polygenic risk scores with GWAS summary statistics.

ABSTRACT:

Background

Polygenic risk score (PRS) is a major research topic in human genetics. However, a significant gap exists between PRS methodology and applications in practice due to often unavailable individual-level data for various PRS tasks including model fine-tuning, benchmarking, and ensemble learning.

Results

We introduce an innovative statistical framework to optimize and benchmark PRS models using summary statistics of genome-wide association studies. This framework builds upon our previous work and can fine-tune virtually all existing PRS models while accounting for linkage disequilibrium. In addition, we provide an ensemble learning strategy named PUMAS-ensemble to combine multiple PRS models into an ensemble score without requiring external data for model fitting. Thr

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC11462675 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature