Kharitonova, Elena V.Sun, QuanOckerman, FrankChen, BrianZhou, Laura Y.Cao, HongyuanMathias, Rasika A.Auer, Paul L.Ober, CaroleRaffield, Laura M.Reiner, Alexander P.Cox, Nancy J.Kelada, SamirTao, RanLi, Yun2024-08-262024-08-262024-05-24Kharitonova EV, Sun Q, Ockerman F, et al. EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints. Preprint. medRxiv. 2024;2024.05.23.24307839. Published 2024 May 24. doi:10.1101/2024.05.23.24307839https://hdl.handle.net/1805/42953Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.en-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalPolygenic risk score (PRS)Genetically correlated traitsEndophenotypeArticle