与多基因评分相比，基于电子健康记录的预测指标的跨生物库通用性和准确性，这一成果由赫尔辛基大学Andrea Ganna研究小组经过不懈努力而取得。该项研究成果发表在2025年8月27日出版的《自然—遗传学》上。

研究组训练了基于弹性网络的PheRS来预测845929人（年龄=32-70岁)，来自芬兰（FinnGen）、英国（UKB）和爱沙尼亚（EstB）的三项基于生物库的研究。所有PheRS均与相关疾病有统计学显著相关性，并且在应用于其他研究时，大多数患者无需再培训即可很好地普遍化。在13种疾病中，PheRS和PGS仅具有中度相关性，与单独的PGS相比，包含这两种预测因子的模型改善了8种疾病的发病预测。他们的研究结果表明，基于EHR的风险评分可以在EHR之间很好地转移，从PGS中获取很大程度上独立的信息，并为疾病风险预测提供附加效益。

据悉，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，类似于多基因评分（PGS）主题遗传信息。虽然PGS的普遍性已经被研究过，但关于PheRS在医疗保健系统中的普遍性以及PheRS是否与PGS互补，人们所知甚少。

附：英文原文

Title: Cross-biobank generalizability and accuracy of electronic health record-based predictors compared to polygenic scores

Author: Detrois, Kira E., Hartonen, Tuomo, Teder-Laving, Maris, Jermy, Bradley, Lll, Kristi, Yang, Zhiyu, Mgi, Reedik, Ripatti, Samuli, Ganna, Andrea

Issue&Volume: 2025-08-27

Abstract: 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.

DOI: 10.1038/s41588-025-02298-9

Source: https://www.nature.com/articles/s41588-025-02298-9