哥伦比亚大学Iuliana Ionita-Laza课题组近日取得一项新成果。经过不懈努力，他们开发出基于电子健康记录表型的易患性阈值模型疾病风险预测。相关论文发表在2025年11月3日出版的《自然—遗传学》杂志上。

在这里，研究小组描述了责任阈值表型整合，这是一种将遗传相关性与表型数据相结合的方法，包括二元和连续特征，如诊断代码、家族病史、实验室测量和生物标志物，以获得新的目标疾病的连续表型。该模型利用一种自动性状选择算法，提高了疾病风险预测的性能，并提供了与目标疾病相关的非目标性状的见解。他们对eMERGE网络和UK Biobank数据的模拟和应用表明，与传统的表型编码相比，在疾病风险预测和全基因组关联研究能力方面取得了一致的性能提升，传统的表型编码模型仅包含家族史和表型插补方法SoftImpute，具有类似的假阳性率控制。

研究人员表示，电子健康记录越来越多地被用作基因组研究的重要资源。然而，来自电子健康记录的临床数据的病例对照标记具有挑战性，大多数研究使用表型代码来定义病例/对照标记，导致次优下游分析。

附：英文原文

Title: Liability threshold model-based disease risk prediction based on electronic health record phenotypes

Author: Lee, Cue Hyunkyu, Khan, Atlas, Wang, Chen, Weng, Chunhua, Buxbaum, Joseph D., Kiryluk, Krzysztof, Ionita-Laza, Iuliana

Issue&Volume: 2025-11-03

Abstract: Electronic health records have been increasingly adopted as useful resources for genomic research. However, case–control labeling of clinical data from electronic health records is challenging and most studies utilize phenotype codes to define case/control labels, resulting in suboptimal downstream analyses. Here we describe the liability threshold phenotypic integration, a method combining genetic relatedness with phenotypic data, including binary and continuous traits such as diagnosis codes, family disease history, laboratory measurements and biomarkers, to derive new continuous phenotypes for target diseases. The model utilizes an automatic trait selection algorithm that increases performance in disease risk prediction and provides insights into nontarget traits associated with the target disease. Our simulations and applications to the eMERGE network and the UK Biobank data demonstrate consistent performance gains in disease risk prediction and genome-wide association study power compared to conventional phenotype codes, models that solely incorporate family history and the phenotype imputation method SoftImpute, with similar false-positive rate control.

DOI: 10.1038/s41588-025-02370-4

Source: https://www.nature.com/articles/s41588-025-02370-4