Joint modeling of effect sizes for two correlated traits: Characterizing trait properties to enhance polygenic risk prediction.

ABSTRACT: Recent years have witnessed a surge in the development of innovative polygenic score (PGS) methods, driving their extensive application in disease prevention, monitoring, and treatment. However, the accuracy of genetic risk prediction remains moderate for most traits. Currently, most PGSs were built based on the summary statistics from the target trait, while many traits exhibit varied degrees of shared genetic architecture or pleiotropy. Appropriate leveraging of pleiotropy from correlated traits can potentially improve the performance of PGS of the target trait. In this study, we present PleioSDPR, a novel method that jointly models the genetic effects of complex traits and identifies conditions under which leveraging pleiotropy can improve polygenic risk prediction. PleioSDPR models the

SUBMITTER: Zhang C 

PROVIDER: S-EPMC12858075 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature