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

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 joint distribution of effect sizes across traits, allowing SNPs to be null for both traits, causal for only one trait, or causal for both traits, and it flexibly captures region-specific genetic correlations and unequal heritability across traits. Through extensive simulations and real data applications, we demonstrate that PleioSDPR improves prediction performance compared with several univariate and multivariate PGS methods, especially when there is no validation dataset. For example, by incorporating information from schizophrenia or leg fat-free mass, PleioSDPR effectively improves the prediction accuracy of bipolar disorder (14.5% accuracy gain) and hip circumference (14.6% accuracy gain), respectively. Moreover, our results indicate that traits with stronger genetic correlations to the target trait, greater heritability, and limited sample overlap contribute more substantially to enhancing prediction accuracy for the target trait. Overall, our study highlights the potential of PleioSDPR to enhance the accuracy of genetic risk prediction by effectively leveraging pleiotropy across traits and diseases. These findings contribute to a broader understanding of polygenic risk prediction and underscore the importance of incorporating pleiotropic information to improve the use of these predictions in disease prevention and treatment strategies.