Large-scale evaluation of proteomic and polygenic risk scores reveals complementary contributions to incident disease prediction

作者：Jakob Woerner, Thomas C. Westbrook, Jaehyun Joo, Manu Shivakumar, Rasika Venkatesh, Tess Cherlin, Sang‐Hyuk Jung, Seokho Jeong, Damián Maseda, Michelle L. McKeague, Shwetank, Matei Ionita, Joost Wagenaar, Sarah Abramowitz, Anurag Verma, Bingxin Zhao, SeungGeun Lee, Scott M. Damrauer, Michael G. Levin, Su‐Jin Heo, Thomas P. Cappola, Daniel J. Rader, Sharlene M. Day, Rajat Deo, Joel M. Gelfand, Ravi Ramessur, Marie Guerraty, Shefali S. Verma, Bogdan Paşaniuc, Marylyn D. Ritchie, Sokratis A. Apostolidis, Allison R. Greenplate, E. John Wherry, Penn Medicine Biobank, Yonghyun Nam, Dokyoon Kim · 发表于：medRxiv · 年份：2025 · DOI：10.1101/2025.07.10.25331242 · 被引用次数：6 · 研究领域：Genetic Associations and Epidemiology、Bioinformatics and Genomic Networks、Liver Disease Diagnosis and Treatment

Plasma proteins capture dynamic physiological processes and may offer more immediate insight into disease risk than static genetic predictors. We evaluated the predictive utility of proteomic risk scores (ProRS) versus polygenic risk scores (PRS) across 301 phenotypes in 39,843 participants from the UK Biobank Pharma Proteomics Project. ProRS, trained on prevalent cases, were tested for incident disease and benchmarked against PRS derived from genome-wide association statistics. Among 268 phenotypes with informative signals, ProRS outperformed PRS in 88% of traits (a median C-index improvement of 9.6%), showing strongest gains for circulatory, metabolic, and immune conditions. Combined models further improved prediction, particularly for traits with higher heritability. Longitudinal analyses showed that ProRS values were elevated years before diagnosis. External validation in 841 Penn Medicine BioBank participants confirmed consistent performance and transferability, with AUC improvements up to 4.18% over PRS alone. Plasma proteomic profiling provides complementary, temporally responsive information that enhances individual-level disease prediction.