Recently polygenetic risk score (PRS) has been successfully used in the risk prediction of complex human diseases. Many studies incorporated internal information, such as effect size distribution, or external information, such as linkage disequilibrium, functional annotation, and pleiotropy among multiple diseases, to optimize the performance of PRS. To leverage on multiomics datasets, we developed a novel flexible transcriptional risk score (TRS), in which messenger RNA expression levels were imputed and weighted for risk prediction. In simulation studies, we demonstrated that single-tissue TRS has greater prediction power than LDpred, especially when there is a large effect of gene expression on the phenotype. Multitissue TRS improves prediction accuracy when there are multiple tissues with independent contributions to disease risk. We applied our method to complex traits, including Crohn's disease, type 2 diabetes, and so on. The single-tissue TRS method outperformed LDpred and AnnoPred across the tested traits. The performance of multitissue TRS is trait-dependent. Moreover, our method can easily incorporate information from epigenomic and proteomic data upon the availability of reference datasets.

中文翻译：

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一种用于复杂人类疾病风险预测的新型转录风险评分

最近，多基因风险评分（PRS）已成功用于复杂人类疾病的风险预测。许多研究结合了内部信息，例如效应量分布，或外部信息，例如连锁不平衡、功能注释和多种疾病之间的多效性，以优化 PRS 的性能。为了利用多组学数据集，我们开发了一种新颖的灵活转录风险评分 (TRS)，其中对信使 RNA 表达水平进行估算和加权以进行风险预测。在模拟研究中，我们证明单组织 TRS 比 LDpred 具有更大的预测能力，尤其是当基因表达对表型有很大影响时。当多个组织对疾病风险有独立贡献时，多组织 TRS 可提高预测准确性。我们将我们的方法应用于复杂性状，包括克罗恩病、2 型糖尿病等。单组织 TRS 方法在测试性状方面优于 LDpred 和 AnnoPred。多组织 TRS 的性能取决于性状。此外，我们的方法可以根据参考数据集的可用性轻松整合来自表观基因组和蛋白质组数据的信息。