Polygenic transcriptome risk scores enhance predictive accuracy in atopic dermatitis
Abstract Background 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-...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
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| Series: | Journal of Translational Medicine |
| Online Access: | https://doi.org/10.1186/s12967-025-06570-8 |
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| Summary: | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
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| ISSN: | 1479-5876 |