Antonatos Charalabos, Budu-Aggrey Ashley, Pontikas Alexandros, Akritidis Adam, Pasmatzi Efstathia, Tsiogka Aikaterini, Gregoriou Stamatis, Grafanaki Katerina, Paternoster Lavinia, Vasilopoulos Yiannis
Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504, Patras, Greece.
MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
J Transl Med. 2025 May 23;23(1):575. doi: 10.1186/s12967-025-06570-8.
Incorporation of gene expression when estimating polygenic risk scores (PRS) in atopic dermatitis (AD) may provide additional insights in disease pathogenesis and enhance predictive accuracy. In this study, we developed polygenic transcriptome risk scores (PTRSs) derived from AD-enriched tissues and evaluated their performance against traditional PRS models and a baseline risk model incorporating eosinophil and lymphocyte counts in the prediction of AD.
We conducted transcriptome-wide association studies (TWAS) using the PrediXcan framework to construct tissue-specific PTRSs. Risk score performance was assessed in 256,888 Europeans (10,816 cases) and validated in an independent cohort of 64,152 Europeans (2669 cases) from the UK Biobank.
We observed a modest correlation between PRS and PTRS, exerting independent effects on AD risk. While PRS demonstrated superior predictive performance compared to single-tissue PTRSs, combining both models significantly enhanced prediction accuracy, yielding a c-statistic of 0.646 (95% confidence intervals: 0.634-0.656). Notably, tissue-specific PTRSs revealed stronger associations with baseline risk factors, where Eppstein-Bar virus (EBV)-transformed lymphocytes and unexposed skin PTRSs tissues reported positive associations with lymphocyte counts.
Our findings highlight the value of integrating transcriptome-based risk models to incorporating additional omics layer to refine risk prediction and enhance our understanding of genetic architecture of complex traits.
在估计特应性皮炎（AD）的多基因风险评分（PRS）时纳入基因表达，可能为疾病发病机制提供更多见解，并提高预测准确性。在本研究中，我们开发了源自AD富集组织的多基因转录组风险评分（PTRS），并在AD预测中评估了它们相对于传统PRS模型和纳入嗜酸性粒细胞和淋巴细胞计数的基线风险模型的性能。
我们使用PrediXcan框架进行全转录组关联研究（TWAS），以构建组织特异性PTRS。在256,888名欧洲人（10,816例病例）中评估风险评分性能，并在来自英国生物银行的64,152名欧洲人（2669例病例）的独立队列中进行验证。
我们观察到PRS和PTRS之间存在适度相关性，对AD风险发挥独立作用。虽然PRS与单组织PTRS相比表现出卓越的预测性能，但将两种模型结合可显著提高预测准确性，产生的c统计量为0.646（95%置信区间：0.634 - 0.656）。值得注意的是，组织特异性PTRS与基线风险因素的关联更强，其中爱泼斯坦 - 巴尔病毒（EBV）转化的淋巴细胞和未暴露皮肤PTRS组织与淋巴细胞计数呈正相关。
我们的研究结果强调了整合基于转录组的风险模型以纳入额外组学层来优化风险预测并增强我们对复杂性状遗传结构理解的价值。
