Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms.

ABSTRACT: With improved whole-genome sequencing and variant imputation techniques, modern genome-wide association studies (GWASs) have enriched our understanding of the landscape of genetic associations for thousands of disease phenotypes. However, translating the marginal associations for millions of genetic variants to integrated polygenic risk scores (PRSs) that capture their joint effects on the phenotype remains a major challenge. Due to technical and statistical constraints, commonly used PRS methods in this setting either perform heuristic pruning and thresholding or overlook most genetic association signals by restricting inference to small variant sets, such as HapMap3. Here, we present a set of algorithmic improvements and compact data structures that enable scaling summary-statistics-base

SUBMITTER: Zabad S 

PROVIDER: S-EPMC12256920 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature