Detrois Kira E, Hartonen Tuomo, Teder-Laving Maris, Jermy Bradley, Läll Kristi, Yang Zhiyu, Mägi Reedik, Ripatti Samuli, Ganna Andrea
Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Nat Genet. 2025 Aug 27. doi: 10.1038/s41588-025-02298-9.
Electronic health record (EHR)-based phenotype risk scores (PheRS) leverage individuals' health trajectories to estimate disease risk, similar to how polygenic scores (PGS) use genetic information. While PGS generalizability has been studied, less is known about PheRS generalizability across healthcare systems and whether PheRS are complementary to PGS. We trained elastic-net-based PheRS to predict the onset of 13 common diseases for 845,929 individuals (age = 32-70 years) from three biobank-based studies in Finland (FinnGen), the UK (UKB) and Estonia (EstB). All PheRS were statistically significantly associated with the diseases of interest and most generalized well without retraining when applied to other studies. PheRS and PGS were only moderately correlated and models including both predictors improved onset prediction compared to PGS alone for 8 of 13 diseases. Our results indicate that EHR-based risk scores can transfer well between EHRs, capture largely independent information from PGS, and provide additive benefits for disease risk prediction.
基于电子健康记录（EHR）的表型风险评分（PheRS）利用个体的健康轨迹来估计疾病风险，这类似于多基因评分（PGS）利用遗传信息的方式。虽然已经对PGS的可推广性进行了研究，但对于PheRS在不同医疗系统中的可推广性以及PheRS是否与PGS互补，人们了解得较少。我们基于弹性网络训练了PheRS，以预测来自芬兰（芬兰基因库）、英国（英国生物银行）和爱沙尼亚（爱沙尼亚生物银行）的三项基于生物样本库的研究中845929名个体（年龄在32至70岁之间）的13种常见疾病的发病情况。所有PheRS与感兴趣的疾病均存在统计学上的显著关联，并且在应用于其他研究时，大多数在无需重新训练的情况下都能很好地推广。PheRS与PGS仅呈中等程度的相关性，对于13种疾病中的8种，包含这两种预测指标的模型相比仅使用PGS的模型，在发病预测方面有所改善。我们的结果表明，基于EHR的风险评分在不同电子健康记录之间能够很好地转移，能够从PGS中获取基本独立的信息，并为疾病风险预测提供附加益处。
