Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer’s disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson’s disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.

中文翻译：

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SR-TWAS：利用多个参考面板通过集成机器学习提高 TWAS 能力

通常存在给定组织或多个组织的多个参考组，并且多重回归方法可用于训练 TWAS 的基因表达插补模型。为了利用用多个参考组、回归方法和组织训练的表达插补模型（即基础模型），我们开发了一种基于堆叠回归的 TWAS (SR-TWAS) 工具，该工具可以获得给定验证转录组的基础模型的最佳线性组合数据集。模拟和实际研究都表明，SR-TWAS 由于有效训练样本量的增加以及跨多个回归方法和组织的借用强度而提高了功效。利用跨多个参考组、组织和回归方法的基础模型，我们对阿尔茨海默病 (AD) 痴呆和帕金森病 (PD) 的研究分别确定了 AD（补充运动区组织）的 11 个独立显着风险基因和 12 个独立显着风险基因PD（黑质组织），包括AD小说6本，PD小说6本。