EndoPRS: Incorporating endophenotype information to improve polygenic risk scores for clinical endpoints-A study in asthma.

Abstract

Polygenic 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 genetically correlated traits. However, these methods do not account for vertical pleiotropy, where 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 and All of Us by leveraging a paired genome-wide association study of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction and transferability 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.