Kharitonova Elena V, Sun Quan, Ockerman Frank, Chen Brian, Zhou Laura Y, Cao Hongyuan, Mathias Rasika A, Auer Paul L, Ober Carole, Raffield Laura M, Reiner Alexander P, Cox Nancy J, Kelada Samir, Tao Ran, Li Yun
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
medRxiv. 2024 May 24:2024.05.23.24307839. doi: 10.1101/2024.05.23.24307839.
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 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.
通过利用相关表型，可以改进复杂疾病的多基因风险评分（PRS）预测。这推动了几种多性状PRS方法的发展，这些方法联合对来自基因相关性状的信息进行建模。然而，这些方法没有考虑性状之间的垂直多效性，即一个性状作为另一个性状的中介。在这里，我们引入了endoPRS，这是一种加权套索模型，它整合了来自相关内表型的信息，以改进疾病风险预测，而无需对基础内表型-疾病关系的遗传结构做出假设。通过广泛的模拟分析，我们证明了endoPRS在各种复杂遗传框架中的稳健性。我们还应用endoPRS，通过利用嗜酸性粒细胞计数（一种相关内表型）的配对全基因组关联研究（GWAS）来预测英国生物银行中儿童期哮喘的风险。我们发现，与许多现有的PRS方法相比，endoPRS显著提高了预测能力，包括多性状PRS方法MTAG和wMT-BLUP，这表明endoPRS在现实临床环境中的优势。
