Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

在这里，我们提出了因果推断的联合组织插补（JTI）方法和孟德尔随机化框架MR-JTI。JTI利用共享的遗传调控，借用不同组织的转录组中的信息，以组织依赖性方式改善预测性能。值得注意的是，JTI在特殊情况下包括了单一组织插补方法PrediXcan，并且优于其他单一组织方法（贝叶斯稀疏线性混合模型和Dirichlet过程回归）。MR-JTI建模变体级异质性（主要是由于水平多效性，解决了转录组范围关联研究解释的主要挑战），并通过I型错误控制执行因果推理。我们明确了基因表达和复杂性状的遗传结构与孟德尔随机化作为转录组范围关联研究的因果推理策略的适用性之间的联系。我们提供了从GTEx和PsychENCODE面板生成的估算模型资源。对生物库和荟萃分析数据的分析，以及广泛的模拟显示，相对于现有方法，JTI的统计能力，复制和因果映射率显着提高。